Pursuing a PhD might have been the most lucrative decision that Tomasz Matusiak has ever taken.Whilst studying at the Wroclaw University of Science and Technology in Poland's third-largest city, he developed chemical sensors made from ceramic materials based on microplasma generators, and electrical components made from a paste of glass and graphite. Now, Matusiak is using this research to solve a bottleneck that plagues the cutting edge of AI development: moving data between where it is stored in memory and where it is handled in the processing unit (for example, the central processing unit [CPU], which handles arithmetic and logical operations; or more specialised graphics processing units [GPUs], which handle computer graphics and digital images) wastes both time and (electrical) power. This limits the extent to which AI models can be scaled up and, of course, harms the environment.What if one could perform all of the computational tasks right where the data are stored? Matusiak thinks his material can do this, and he has started a company, SemiQa, and produced a system inspired by the human brain, the Analog Neural Network (ANN). Unlike conventional chips, which can reach 80°C and require a cooling system, his ANN system only reaches a maximum of 40°C. SemiQa's goal since its inception at the start of 2025 has been to conquer the universe of data centres, replacing their graphic cards (and the GPUs that power these graphic cards) with ANNs.Matusiak wants to bring back analog processing for its computational advantages. He uses the analogy of a train ride through the countryside. One might look outside the window and see a forest pass one by, followed by a short section alongside a river, before heading back into the forest again. A human brain – the analog system – would see a forest and then not think about it again until it sees a change in the environment (the river), and then once again not actively register the river again until the river has been replaced by the forest. It only processes the changes.But a digital system would constantly process what is outside the window. Analog processing thus saves on energy as it doesn’t process when there hasn't been any change.Likewise, digital processing might allocate a large number of bits to a small integer – for example, even though the number 5 can be expressed in binary with just three bits (101), it might be stored in an 8-bit or a 16-bit structure, where most of the surplus bits are zeroes. Many of the operations performed on these small integers will also result in small integers, so most of the leading zeroes will not change. A lot of memory is wasted.Analog processing can get around this problem by simply storing the 5 in a memory cell as a 5 instead of in eight memory cells as 00000101. (Analog memory cells, unlike digital memory cells, can take on more values than just 0 and 1.)The neural approach is based on a special electrical component called a memristor (short for memory resistor). A traditional resistor follows Ohm's Law, which states that the current (the rate at which electric charge flows) through a conductor is proportional to the difference in voltage (or the difference in electric potential energy, or the work it would take to move a unit of charge provided by, for instance, a battery) across that conductor. Mathematically, Ohm's law is V= IR, where V stands for the voltage, I for the current, and R for the resistance of the conductor, a proportionality constant that indicates how difficult it is for charge to move. The higher the resistance, the lower the current (for a given level of voltage).In a traditional resistor, the resistance doesn't vary with current (or voltage). In a memristor, though, the resistance depends not just on the current (or voltage) but also on the past levels of current running through it (or voltage controlling it). In other words, if the voltage goes up and then goes back down to its earlier level, the current and resistance might not return to their original levels. This ability to take on a range of values of resistance also mean that the memristor can be analog – in other words, that it can represent a range of values and not just a 0 or a 1.In addition to its superior thermal properties, SemiQa's ANN1000 is more power-efficient than other chips, being able to carry out more than 30 TOPS (or 30 trillion operations per second) per Watt of power; standard GPUs or NPUs (neural processing units, which are specialised for AI applications) can only carry out 1-2 TOPS per Watt. (The chip consumes 2.5 Watts of power, and so can carry out roughly 75 TOPS.)It is also naturally faster – ANN1000's latency (the time delay between when the processor requests something from memory to when the processor retrieves it) is 50 times shorter than that of a conventional GPU or NPU.The next step is, of course, commercial-scale production of their chips. They already demonstrated a proof-of-concept of their memristive technology at last year's SEMICON Taiwan, an annual trade show and Asia's largest semiconductor event. They will now create a neural network on silicon and hope to have a product-ready chip tailored to specific applications by the end of 2027. The memristive material, a mixture of organic and inorganic parts, is compatible with CMOS (complementary metal-oxide-semiconductor) technology, which is commonly used in foundries to fabricate chips. Matusiak envisions SemiQa's chips in mission-critical applications where efficient power consumption and processing is highly advantageous. These include autonomous systems, such as drones to be used in war and marine robots. Electric cars can also benefit: GPUs currently account for roughly half the cost of driverless vehicles, and replacing conventional GPUs with SemiQa's chips could reduce the price for consumers whilst maintaining manufacturers' margins. SemiQa also plans to add B2B applications such as data centres to the aforementioned B2C applications. They will tackle this through the ANN2000, a matrix of a thousand smaller ANN1000s.The prize money from the Best AI Awards pales in comparison to the 3 million EUR in pre-seed funding that SemiQa has already raised in Europe. But Matusiak is most grateful for the recognition that the judges have given his company's achievements since they started it just a little more than a year ago. This will also facilitate their collaboration with potential partners – in fact, they are already in talks with two local foundries to deepen their co-operation and scale up production of their chips."If you want something special, you need to work with the special forces," says Matusiak. "Everyone knows that Taiwan is the best in the world."SemiQa also plans to set up a branch office in Taiwan and will potentially hire two business developers in the country in the short term. They also know that they will need more funding, and are looking into perhaps raising money from Taiwanese investors. SemiQa already has a strong relationship with Taiwan, being a member of the Taiwan-Poland Chamber of Commerce and having signed memoranda of understanding with several Taiwanese businesses.The Best AI Awards celebrate global excellence in artificial intelligence and IC design, welcoming submissions from innovative companies and brilliant student teams. Following the success of the 2026 edition—advised by the MOEA, organized by DoIT, and executed by TCA—the prestigious competition is officially transitioning into an annual tradition.Offering substantial grand prizes and unmatched industry exposure, the countdown to Best AI Awards 2027 has already begun. Details on the next submission cycle, prize tiers, and eligibility rules will be released soon. Connect with us on LinkedIn for the latest official updates and application alerts. 

The Best AI awards were given out in two categories: artificial intelligence (AI) applications and integrated circuit (IC) design. AIYO was entered in the IC design category, but their concept – AI-powered acceleration of IC design – really straddles the two categories.Chip design is a long, arduous process. Today's chips contain billions of transistors, and designers not only have to design them to do what they want them to do, but have to ensure that the design satisfies a large number of rules and can actually be physically produced in a foundry. Since 2010, the number of transistors and gates has increased by nearly a hundredfold, but engineering productivity (as measured by the number of gates that a chip designer can design in a day) has increased by only about three times. This 29-times gap is set to widen even further over the next few years. In other words, it takes chip engineers more time than ever before to do their job.The problem lies not solely in the number of components on a chip but in the complexity in how they interact. The demand for custom silicon – ICs specially designed and optimised for specific applications or customers – and the sheer number of use cases now being designed for is growing, but it is getting harder and harder to find the engineering talent for custom silicon. Specialised silicon requires specialised talent.Tier-one design houses, like NVIDIA or MediaTek, can still find talent, but it's a different story for tier-two companies. Chip design is increasingly a bottleneck for technology companies to implement their AI-powered (and non-AI-powered) solutions.AIYO thinks that AI can help with that. It aims to reduce the gap between tier one and tier two companies, without completely replacing human design and the need for engineers. Engineers use natural language to provide their design specifications, including the constraints and requirements that the chip must satisfy. AIYO's AI agent then generates Verilog, which is a piece of code that describes the design of digital circuits.This Verilog must satisfy the specifications that the user set out. AIYO's agent can also optimise it along various metrics, such as for a lower power consumption, a higher performance (in other words, how fast the chip operates, as measured by its clock speed or data throughput), or a smaller area (PPA) are the most commonly invoked. Different chips prioritise these three factors (collectively known as PPA) differently, but AIYO can trade off, for example, a lower performance for a lower area and less power consumption.AIYO's agent then performs a closed-loop verification of the design to ensure that it is feasible. The agent reviews the error logs and iterates the design until the design passes all tests. Then – at least theoretically – the design is ready for tape-out, or actual production of the circuit at the foundry. Tape-out is an expensive process, costing in the millions of dollars, so it is essential that the finalised design performs as expected.AIYO uses RISC-V, an open-source instruction set architecture (ISAs) that has grown exponentially in popularity since its introduction in 2014; it has already been used in more than 20 billion cores. This avoids the need to pay a licensing fee to the more common (but not open-source) ISAs in use today, such as ARM or x86.The performance of IC-design agents can be measured using a standard set of test problems. Can the agent solve the problems (i.e., design a suitable IC) on the first pass? And can it solve a different set of IC design problems eventually, after however many iterations? AIYO performs at the head of the pack in both models, a little bit ahead of NVIDIA's VerilogCoder agent and far ahead of ChatGPT, DeepSeek, and Claude. One to two engineers are now required to design a chip, where three to five would have been needed before. Furthermore, it now takes these one or two engineers two months to design a chip (and they can test multiple designs simultaneously), whilst those three to five engineers in a traditional design house would have needed six months. This is an improvement in efficiency of roughly an order of magnitude.AIYO is still a work in progress, and humans are needed to check for any mistakes the engine might make – human engineers' jobs are safe, at least for now. But AIYO does makes it possible to compress the iteration cycle and for even tier-two chip design companies to realise their designs with limited human resources.One of AIYO's most pressing next steps is expanding its customer base – not only for commercial reasons, but also because this will expand their training data and thus improve their AI engine. AIYO currently has one customer who needs help designing custom FPGA (field-programmable gate array) integrated circuits. AI is, by default, a generalist, and its large language models can fail when faced with very specific use cases that it has yet to see. Helping this customer with its specific use cases can help the AI engine gain specialist skills, and the more customers that AIYO can obtain, the more versatile the tool will be.To this end, the team is expanding the set of design problems. A large set of open-source IC design problems already exists, but AIYO is also working on building their own set of synthetic design problems.The money from the Best AI Awards is great, says Ballard, but realistically, it is not even enough for one year's access to a top EDA (electronic design automation) tool – training AI models is expensive. He says that the biggest benefit from the awards is the people it has allowed them to meet, and in particular, the conversations they've been able to have. "They'll ask, 'Did you consider X, Y, and Z?' Sometimes yes, we have, but sometimes, we have an action item for the future."This also gives them an opportunity to enter talks with various venture capital investors and potential Taiwanese partners. AIYO is currently working with funding provided by the co-founders themselves, but they're hoping to find a Taiwanese venture capital investor in the next few months.Tang-Hung Po and Austin Ballard have been working on AIYO for only roughly a year. Po, originally from Taiwan and now based back in the country, obtained his master's in electrical engineering and computer science from the University of Michigan; he is the company's primary engineering lead. He brings more than 20 years of experience in SoCs (systems on a chip) and ASICs (application-specific integrated circuits) to AIYO, and was previously a director and a chief technical officer at other companies.Ballard, an American based in Seattle but with a Taiwanese mother, brings his experience scaling operations at Meta, Amazon and TikTok to now handle anything at AIYO not related to engineering,  The time difference allows them to collaborate during Ballard's evenings and Po's mornings, and their almost diametrically opposite locations, along with their different skill sets, facilitates engagement with all sorts of partners on both sides of the world. (As a side benefit, Ballard now has a business reason to visit Taiwan!)The Best AI Awards celebrate global excellence in artificial intelligence and IC design, welcoming submissions from innovative companies and brilliant student teams. Following the success of the 2026 edition—advised by the MOEA, organized by DoIT, and executed by TCA—the prestigious competition is officially transitioning into an annual tradition.Offering substantial grand prizes and unmatched industry exposure, the countdown to Best AI Awards 2027 has already begun. Details on the next submission cycle, prize tiers, and eligibility rules will be released soon. Connect with us on LinkedIn for the latest official updates and application alerts.

Cardiovascular disease – diseases of the heart or the blood vessels that move blood to and from the heart – were responsible for an estimated 32 per cent of deaths worldwide, or just under 20 million deaths, in 2022, according to the World Health Organisation. Their takeaway? "It is important to detect cardiovascular disease as early as possible so that management with counselling and medicines can begin."Electrocardiograms (ECGs), a series of peaks that represent each heartbeat, depict the heart's electrical activity over time. An abnormal ECG (compared to one's baseline) can be indicative of arrhythmias, the medical term for irregular heartbeats that, in most cases, are not serious but can sometimes lead to strokes, heart attacks, and death. Patients can wear Holter monitors to continuously measure their ECG, but that entails placing a large number of electrodes (between three and eight, and up to twelve for greatest accuracy) on the skin, and wearing a piece of recording equipment around the neck or waist. Not only can this be inconvenient, but up to half of all patients reported some sort of skin irritation due to the electrodes.Smartwatches can also be used to measure an ECG, but only when both hands touch the device – in other words, they cannot be used for passive, continuous heart monitoring. That's where integrated circuits can help. Students at the Hanoi University of Science and Technology (HUST) are working on an integrated circuit that can continuously monitor the heart's electrical activity through the ear. Such devices are already commonplace, with many people wearing hearing aids or smart hearables for at least some of the day. There are three physical connection points – both ears and one earlobe – the minimum required to measure an ECG.The ECG collected between the ears is useful only insofar as it can then reconstruct what lead-1 ECG, which is the electrical activity as would be measured by electrodes placed on the right and left arms. The lead-1 ECG is also a standard ECG measurement that is used for diagnosis and monitoring arrhythmias. There are generally similarities between the ear ECG and the lead-1 ECG – in particular, the peaks (representing the heartbeats) appear at the same location – but they are clearer in the lead-1 ECG than in the ear ECG.So while the algorithm on the chip must be able to recover the shape of the original lead-1 ECG with as little noise as possible, it should not smooth over any possible signs of abnormality – in other words, it needs to be sensitive enough to detect arrhythmias when they are present.The system is relatively unobtrusive and runs on low power, but using the ear also presents disadvantages. The signal-to-noise ratio is low. In fact, simply shaking one's head or talking will introduce noise into the measurements. Furthermore, special data privacy concerns when collecting biological signals – each user has a unique ‘heartprint’ and some users might be particularly wary of sending such data to another machine – make it imperative that any analysis is done on the chip itself.The user himself can measure his own lead-1 ECG using his two fingers (as proxies for the right and left arms), and measure his ear ECG with the devices touching his ear. All of this is done with electrodes attached to a sensor (with a built-in analog-digital convertor, or ADG) developed by Texas Instruments. Two datasets were created to train the AI calibration algorithm for the conversion between the ear and lead-1 ECGs. Firstly, the team collected its own dataset, measuring the ECGs for 45 patients for 10 minutes each. A synthetic public dataset was also created by modifying an existing large, open dataset of ECGs, the PTB-XL. This dataset doesn't include ear ECGs, so the team added noise to existing lead-1 measurements (in an attempt to emulate the ear ECG) and tried to recover the original, non-noisy lead-1 ECG. The team, which calls itself EDABK Brain, was able to bring the algorithm's latency, or delay time between receiving the ear ECG and producing the lead-1 ECG, down to below 50 milliseconds. Such short times obviate the need to store data from the ear-ECG, for instance. At the same time, they maximised the utilisation of the processing element, meaning that they worked hard to make sure the chip was effective.After prototyping a field-programmable gate array (FPGA) with their IC design, the team trained it on the self-collected dataset. On the two most important metrics, the signal-to-noise ratio and the correlation with the true lead-1 ECG, EDABK Brain slightly outperformed state-of-the-art algorithms. It was edged out by another algorithm – but the HUST model used fewer than a quarter as many parameters as that algorithm did.For another comparison, EDABK Brain's circuit used less power and was more energy efficient than BioGAP, a leading biosensing platform that can measure ECGs as well as other electrical signals in the body. However, BioGAP's circuit has a lower latency time and a high throughput (measured in operations per second).The team behind this chip design, the EDABK Brain Team (the EDA stands for electronic design automation, and the BK stands for Bách khoa, which is in the Vietnamese name of their university,  consists of Phuong Linh Nguyen, a student who graduated from their university last year and is now studying for a master's degree at Télécom Paris (one of the most prestigious French grandes écoles and part of the Polytechnic Institute of Paris), and who spoke to DIGITIMES; and her two former classmates, Thanh Dat Do and Duc Tu Nguyen, both of whom are in their final year in the School of Electronics and Electrical Engineering at HUST; and their supervisor, Duc Minh Nguyen. Having the chance to participate in the Best AI Awards motivates Nguyen and her teammates. Being just students at the start of their scientific career, they were curious to know what industry professionals thought of their idea – does it have potential? Winning the bronze medal is confirmation that it indeed does have potential.Nguyen said that it was also a relatively rare opportunity for them to communicate their ideas in a non-academic setting. Their university was able to send three teams to the finals, which also entailed a trip to Taiwan and interactions with state-of-the-art AI and IC researchers.They will now focus on writing a paper – after all, they come from academia – and preparing patent applications. On the technical side, they want to reduce the number of bits in their resolution – in other words, see whether they can convert the analog signal to a digital one with a fewer number of bits and less accuracy – to reduce complexity and thereby power consumption. The team will also experiment with other electrodes.The Best AI Awards celebrate global excellence in artificial intelligence and IC design, welcoming submissions from innovative companies and brilliant student teams. Following the success of the 2026 edition—advised by the MOEA, organized by DoIT, and executed by TCA—the prestigious competition is officially transitioning into an annual tradition.Offering substantial grand prizes and unmatched industry exposure, the countdown to Best AI Awards 2027 has already begun. Details on the next submission cycle, prize tiers, and eligibility rules will be released soon. Connect with us on LinkedIn for the latest official updates and application alerts.

Artificial intelligence is just an interesting theoretical problem for many scientists and engineers, but it is at its most useful when it directly responds to the needs of its users.That's why Amity Solutions, a component company of Thailand-based Amity Group, developed Eko Agentic, a data analyst for store managers. They were long-standing consultants to one of the biggest retail chains active in Thailand and Malaysia with thousands of stores in the region. Executives at this retailer told Amity Solutions that they had tried to use various AI tools to improve the efficiency of their store management, but that these tools were not adequate for their needs.Store managers, stock replenishers, and other frontline workers on the store floor have to handle numerous disconnected tasks on a daily basis. They might use dashboards to monitor various store performance metrics, but synthesising the disparate information into business decisions can be complicated, with store managers resorting to past experience and guesses. Inexperienced store managers, in particular, might be unable to respond effectively to new situations or to best implement requests from headquarters.What if AI could take over the data analysis from store managers? Amity Solutions developed Eko Agentic to do just this: It is trained with data on how the top-performing store managers across the retailer's large network would respond to various business situations, and then rolled out across other stores, taking into account each store’s particular characteristics. The goal is to reduce extra, unsellable stock; to avoid empty shelves; and to better time and set up promotions. This way, the retailer tries to make all stores as efficient as those run by the best store managers.In its first iteration of Eko Agentic, Amity Solutions identified those stores that consistently outperformed the average, both through looking at store performance metrics and by talking to headquarters. Positive outliers were also identified in different environments – for example, the best inner-city markets (which tend to be smaller) and the best rural hypermarkets (which tend to be larger) – in order to get the widest possible range of data.Amity Solutions then sent teams to perform interviews at each of these stores, asking frontline workers to explain how they would think through various situations. What would they do if sales dropped by 5 per cent year-on-year? Perhaps the store manager would first check the basket size, then check the average value of each item in the basket, and then check for the use of special promotions.AI – and in particular, a technique developed by Amity Solutions called reflective optimisation via automated debugging (ROAD) – then structured these interviews into decision trees that visualised the store managers' train of thought. Most optimisation methods so far rely on large data sets for testing and calibration, but these interviews with store managers at Lotus's produced a smaller data set, something that ROAD's algorithm could work with. This was especially important in the Thai context because most large language models are trained on Western datasets, but differences in culture and the business environment between the West and Thailand (e.g., in the availability of parking lots) meant that other models, trained on larger data sets, weren't necessarily immediately applicable.The model was then applied to each individual store, generating a strategy that had been optimised for each one. Reinforcement learning (a paradigm within machine learning that seeks to optimise the impact of an agent's actions based on continual feedback to that agent from those impacts) is then used to further optimise store managers' strategies.Despite this rather simplistic description (and its correspondingly smaller size), Eko Agentic has been remarkably effective in data analytics. It is cheaper than many other AI tools (such as Claude and ChatGPT), and outperforms other state-of-the-art LLM and AI data analysis agents in an industry-standard set of real-world problems, the Data Agent Benchmark for Multi-step Reasoning (DABStep). It achieved 41 per cent accuracy in resolving DABStep tasks – the highest amongst all such agents – whilst its nearest competitor, Microsoft, only achieved 32 per cent accuracy; Anthropic's, OpenAI's, and Google's systems lagged even further back.Eko Agentic is also now able to outperform human analysts working at the Thai retailer. A blind test was conducted, wherein both Eko Agentic and a human analyst performed an analysis on various real business problems. Store managers then select the better of the two responses, without knowing who composed each one. The first versions of Eko Agentic still performed below a human analyst, but the latest version – the fifth – gives, on average, suggestions that are favoured over those from a human analyst. There are only a few supermarket chains in Thailand, and Amity Solutions is, of course, unable to work with the competitors to the retailer it currently works with. However, their methodology is applicable to other retail applications – and in fact, Amity Solutions is currently using Eko Agentic to help a telecommunications giant in Thailand manage its mobile phone shops. Amity Solutions is also looking for opportunities to apply Eko Agentic to supermarket chains in other Southeast Asian countries.A potential limitation with basing decisions on what the best store managers would do is that one might be limited to – and thus not be able to improve on – how well the best store managers do. In other words, you can interpolate performance but it is uncertain whether you can extrapolate to even superior strategies.Thus, as one of its next steps, Amity Solutions is creating a large behavioural model (LBM) that serves as a stand-in for customers. It is a digital twin that simulates customer behaviour, and models that respond to this LBM can potentially outperform the current best store managers.Amity Group, with offices in Thailand, Malaysia, Singapore, Australia, India, the United Kingdom, and the United States, employs 800 staff members over five companies in various realms of AI. Amity Solutions, the business unit that commissioned Eko Agentic through its long-standing collaboration with the aforementioned retailer, is based in Bangkok and employs 150 employees. However, it was Amity's AI Research and Application Center (ARAC), whose small team of just 15 staff members deploys generative AI solutions across all of Amity's daughter companies, that developed the technology behind Eko Agentic. Currently based in Thailand, they aim to stay at the forefront of developments in AI, says Touchapon Kraisingkorn, the chairman of ARAC and the executive director of Amity – and thus they have plans to expand ARAC to Singapore.Winning at the Best AI Awards is, says Kraisingkorn, validation that they are "one of the world-class labs that creates an effective product and solves real-world problems". The earnings from this award will help them jump-start hiring in Singapore. They are also open to opportunities for collaboration with Taiwanese companies in chips and robotics. The Best AI Awards celebrate global excellence in artificial intelligence and IC design, welcoming submissions from innovative companies and brilliant student teams. Following the success of the 2026 edition—advised by the MOEA, organized by DoIT, and executed by TCA—the prestigious competition is officially transitioning into an annual tradition.Offering substantial grand prizes and unmatched industry exposure, the countdown to Best AI Awards 2027 has already begun. Details on the next submission cycle, prize tiers, and eligibility rules will be released soon. Connect with us on LinkedIn for the latest official updates and application alerts.

As artificial intelligence (AI), high-performance computing (HPC), high-speed networking, and edge computing applications continue to expand rapidly, global demand for custom ASIC solutions is rising at an unprecedented pace. Faced with the escalating design complexity and development costs associated with advanced process nodes at 6/5/4/3nm, balancing time-to-market, cost efficiency, and production quality has become a critical challenge for IC design companies and system vendors worldwide.PGC (TPEx: 8227), with over 35 years of expertise in ASIC design services, is a member of the TSMC Design Center Alliance (DCA) and a Synopsys IP OEM Partner. With a track record of more than 1,500 tape-out projects and over 100 tape-outs completed annually, PGC delivers comprehensive capabilities spanning advanced-node design, APR (Automatic Place and Route), back-end design services and tape-out foundry services, and volume production ramp. In response to growing market demand for advanced-node ASIC development, PGC announces the further enhancement of its Customer-Owned Tooling (COT) business model, integrating ASIC design services, IP resources, and semiconductor supply chain ecosystems to help customers shorten chip development cycles, accelerate tape-out, and rapidly secure engineering samples and production capacity.COT Business Model: Balancing Design Ownership with Development EfficiencyThe COT business model enables customers to retain ownership of critical design assets — including IP, EDA tool licenses, and design data — preserving their core intellectual property and technical autonomy while leveraging PGC's professional ASIC design team and proven design flows to jointly complete chip development. Compared to traditional turnkey models, COT effectively reduces long-term NRE (Non-Recurring Engineering) investment, eliminates vendor lock-in, and enhances flexibility for product iteration, multi-project development, and cross-generation platform continuity.In practice, customers adopting the COT model have achieved development cycle reductions of over one month, along with long-term NRE cost savings of more than 10%, giving them greater autonomy and strategic flexibility in product planning and technology roadmap execution.Synopsys IP OEM Partnership: Lowering IP Licensing Barriers and Accelerating DevelopmentAs a Synopsys IP OEM Partner, PGC provides customers with comprehensive Synopsys IP licensing and integration services. Customers can flexibly incorporate market-proven, high-quality IP based on project requirements, simplifying licensing processes, lowering upfront investment thresholds, and accelerating IP integration and verification through PGC's expertise — further shortening ASIC development cycles and time-to-market.ASE Packaging and Test Integration: Bridging Design to Volume ProductionIn the area of packaging and test, PGC has established a close collaboration with ASE Group, integrating advanced packaging and test resources to provide customers with comprehensive production support from wafer to finished product. This collaboration covers BGA, Flip Chip, and Wire Bond packaging and full test services, helping customers accelerate production ramp while ensuring shipping quality and reliability.One-Stop Solution: End-to-End Support from Design to Mass ProductionBeyond ASIC design services, PGC offers a diverse range of prototyping options, including TSMC CyberShuttle (suited for early-stage design verification, leveraging multi-project wafer sharing to reduce prototyping costs) and VIS (Vanguard International Semiconductor) MPW (suited for specialty process or mature node requirements), enabling customers to complete engineering sample verification with maximum flexibility.PGC delivers a comprehensive one-stop solution encompassing ASIC design, IP integration, APR, DFT, tape-out, prototype verification, packaging and test (OSAT), and volume production ramp，complemented by professional back-end design services  and complete tape-out foundry services, helping customers rapidly obtain engineering samples, complete product validation, and seamlessly transition to mass production, significantly compressing time-to-market.PGC CEO Fred Lai stated: "In a market environment where AI and HPC applications continue to drive demand for advanced-node solutions, customers need more than a design service provider — they need a comprehensive partner capable of integrating IP, design, prototyping, and volume production. By enhancing our COT business model and deepening our three-way ecosystem collaboration with TSMC, Synopsys, and ASE, our goal is to help customers reduce their ASIC development cycles by more than 10%, bringing innovative products to market faster."Looking ahead, as demand for AI inference chips, HPC accelerators, and high-speed networking ASICs continues to grow, PGC will continue to expand its advanced-node service capabilities and further extend its customer reach into the US market, empowering global customers to seize opportunities in the advanced-node semiconductor landscape.PGC Boosts COT Model, Integrates TSMC Ecosystem for ASIC Growth. Credit: PGC

Smiths Interconnect, a Molex company, announces its top performing distributors in 2025. The Distribution Awards program honors distributors which have made a meaningful impact on the growth of Smiths Interconnect across its three key regions: the Americas, EMEA, and Asia.Award recipients in each region are selected based on their outstanding performance compared to the previous fiscal year and on outstanding local service and support. This year, the strong commitment and dedication shown by distributors worldwide have resulted in a larger group of recognized companies. Seven distributors in total have been distinguished for their exceptional achievements.In the Americas, FDH Electronic Products Group, LLC. has been named Distributor of the Year for the second consecutive year. This recognition reflects FDH's outstanding performance, strong partnership, and unwavering commitment to delivering exceptional service and support to its customers.Over the past year, FDH achieved impressive sales growth, demonstrating not only market strength but also a deep dedication to driving their mutual success. Their ability to navigate challenges, adapt quickly, and maintain focus on execution has been instrumental in delivering consistent results and strengthening the partnership.The second distributor recognized in the Americas as Highly Commended Distributor for Business Growth 2025 is Arrow Electronics.Through a focused and strategic customer approach, Arrow has delivered impressive business growth, reinforcing their position in driving market expansion and customer engagement.Arrow's commitment to collaboration, responsiveness, and execution has played an important role in achieving these results. Their team has consistently demonstrated the ability to identify opportunities, adapt to evolving customer needs, and deliver value across joint initiatives.In the EMEA region, Smiths Interconnect has recognized two distinct distributors for their outstanding growth throughout the year.RFMW Ltd is recognised for delivering the strongest growth in 2025 across the company's RF component product lines, reflecting exceptional commercial execution and deep customer engagement. Their results in key South European territories were achieved through a close, collaborative partnership that enabled both organisations to navigate a highly competitive landscape effectively.RFMW consistently converted design activity into revenue while expanding presence in target markets, demonstrating a shared commitment to technical excellence, partnership, and sustained value creation.For the Connectors product line, the top performing distributor was Heilind Electronics GmbH, which supported sales growth with a strong focus on the DAC countries.This success was also driven by well-managed inventory levels, enabling fast deliveries and reliable local support for customers.Last but not least, Asia was one of the most active areas from a distribution standpoint. Three key distributors were appointed: Fusoh Shoji Co., Ltd, Bizmile Co. Ltd, and Conn-Tek Electronics Inc.First, Fusoh Shoji Co., Ltd was named Distributor of the Year 2025 for the Fiber Optic and Components product lines. The company has demonstrated outstanding performance in expanding these product lines in Japan, playing a key role in strengthening its market presence and supporting a solid foundation for future growth.Meanwhile, in the Connectors product line, Korea's Bizmile Co. Ltd secured the Distributor of the Year 2025 title. Awarded for its outstanding contribution to business growth, design-in excellence, and value delivery, Bizmile demonstrated exceptional support to key accounts by driving significant growth through strong inventory commitment and strategic project support. The company also excelled in design-in activities, successfully contributing to major defense programs while consistently delivering measurable value to customers. Its strength in commercial execution, strategic account development, and an ecosystem-driven approach has enabled sustained growth, improved profitability, and strong future demand visibility.Rounding out the region's success, Conn-Tek Electronics Inc was named Distributor of the Year 2025 for the Semiconductor Test product line for the second consecutive year. This award recognizes Conn-Tek's exceptional performance across business growth, design-in excellence, and strategic market development. The company achieved remarkable revenue growth in both China and international markets while demonstrating strong leadership in design-in activities with key customers. Furthermore, Conn-Tek experienced significant expansion in high-value product segments—particularly with the DaVinci coaxial test socket—and successfully penetrated new customer markets. Its design-led, partnership-driven approach has delivered sustained growth, improved profitability, and enhanced strategic value across the Asia region.

Artificial intelligence (AI) is moving beyond a tool that simply answers questions and into the era of "agentic AI"—systems that make their own judgments and carry out tasks. A major event offering a comprehensive view of the latest currents in the global AI industry is once again coming to Seoul.DMK Global, COEX, and the Korea International Trade Association (KITA) announced that they will host "AI Summit Seoul & EXPO 2026" (AISE 2026) over three days, from August 19 (Wed) to 21 (Fri), at COEX in Seoul. The conference will take place in the Grand Ballroom, while a large-scale exhibition (EXPO) runs concurrently in Hall B. Since its inaugural edition in 2018, the industrial-AI-focused event—now in its ninth year—has established itself as an annual global AI event held in Seoul.The central theme this year is putting AI to real work, beyond mere adoption. As AI technology advances rapidly, companies now face a new challenge that goes beyond "whether to adopt AI" to "what authority to grant AI, and how to trust the results it produces." Generative AI has proven its value in producing answers and content; more recently, attention has turned to agent-based AI that independently makes decisions and takes action within enterprise workflows.Reflecting this shift, AISE 2026 has set its theme as "The Transformation Era: Beyond Adoption – Rise of Enterprise Agents." Rather than simply introducing technology, the event focuses on how AI agents are being applied in real business settings and how they are reshaping organizations and business models.The two-day conference, held in the COEX Grand Ballroom beginning August 19, is organized around six core themes: "AI Mega Trends," surveying fast-moving technology and market shifts; "AI Transformation," addressing changes to organizations and business models; "AI & Data," covering foundational challenges such as data labeling and MLOps; "Vertical Industry Use Cases," presenting real-world applications by sector; "AI + Robotics," exploring the convergence of AI and robotics; and the year's most talked-about theme, "Agent & Agentic AI."The global speaker lineup also stands out. Speakers include Steve Chien, a researcher at NASA's Jet Propulsion Laboratory (JPL) who studies autonomous AI capable of independent decision-making in space; Larry Heck, a Georgia Tech professor and leading authority on conversational AI; Maxim Afanasyev of Google Cloud; Hoifung Poon, a researcher at Microsoft Research focused on AI-driven scientific discovery; and Maxime Labonne, a researcher at Liquid AI and an expert in frontier small models.In addition, representatives from leading global technology and industry companies—including Genspark, Google DeepMind, Hyundai, DHL, Notion, IDEO, Mercedes-Benz, Seagate, Adobe, and ClickHouse—will take part to share proven use cases and technical insights across industries.Running alongside the conference, the exhibition (EXPO) takes place in COEX Hall B over three days, from August 19 to 21. Roughly double the size of last year's edition, it will feature more than 100 exhibiting companies and over 30 speech and workshop sessions. A key highlight is the opportunity to survey the entire AI value chain under one roof—from LLMs and generative AI to AI hardware and infrastructure (GPUs, NPUs, and more), robotics and autonomous technologies, and industry-specific solutions. Visitors can experience live demonstrations of products and solutions already in operation at exhibitor booths, across categories including Industry AI, Enterprise AI, generative AI, AI platforms and infrastructure, and physical AI and robotics.Exhibiting companies will run demos and consultations for decision-makers from Korea and abroad, and the program includes 1:1 business matching and networking parties connecting executives, developers, investors, and researchers. EXPO visitor registration is free until July 31 (Super Early Bird); from August 1 it moves to Early Bird pricing of KRW 10,000, and to a standard rate of KRW 20,000 thereafter.Supporting programs have also been strengthened. During the event, offerings include hands-on workshops for in-depth, practical learning with leading AI technologies and solutions from Korea and abroad; investment matching connecting startups with investors; and an AI experience zone. Beyond simple viewing, the event is designed to serve as a venue for experiencing technology firsthand and connecting AI companies with industry, investors, and talent.As AI spreads beyond individual services and solutions into every facet of enterprise operations, the event is expected to offer a chance to survey the latest trends at a glance and to gauge the potential for real-world adoption and commercialization.

EDOM Technology(TWSE: 3048), Asia's best solutions provider, celebrates its 30th anniversary this year. Since its establishment in 1996, EDOM has worked with global partners to lead innovative technologies and witnessed the growth trajectory of the electronics industry along the way. Faced with the booming trend of diversified AI applications, EDOM will focus on four major innovation areas including power technologies, cybersecurity, optical communications, and biomedicine. Through complete technical support and supply chain services, EDOM will help customers accelerate the implementation of AI, smart networking, and edge computing applications, and jointly promote the development of smart manufacturing, smart medical care, and next-generation digital infrastructure.1996 is a critical year in the history of science and technology. This year, the Internet began to fully enter public life, and the core technology, software, and hardware that laid the foundation for modern digital life were born at this time. The company encountered the Asian financial crisis in 1997 when it was first established, but the rise of the Internet from 1996 to 2000 also brought excellent development opportunities. In its early days, EDOM was optimistic about the emerging 3D graphics chips, modem chips, and radio frequency components. Using this as a starting point, EDOM gradually established a foothold in the electronic circuit market and wrote important milestones along with the changes in the industry.Since its listing in 2002, EDOM has continued to deeply explore the Asia-Pacific market. Following the trend of the Asia-Pacific region becoming an important manufacturing base for the global information industry, EDOM has actively expanded its service base and strengthened its supply chain support capabilities. With the rapid development of the technology industry, EDOM has experienced steady growth: in 2010, driven by the popularity of smartphones and tablets, annual revenue exceeded US$1 billion for the first time; in 2014, it seized the opportunity of the rise of mobile communications, with the Internet of Things and mobile payment, annual revenue exceeded US$2 billion; in 2019, benefiting from the growth in market demand for wearable devices and network communications, annual revenue exceeded the milestone of US$3 billion. Facing a new wave of industrial changes driven by AI, EDOM achieved annual revenue of US$3.7 billion in 2025, ranking among the top ten electronic component distributors in the world, and continues to move towards the revenue target of US$4 billion.Over the past thirty years, EDOM has witnessed the birth of epoch-making applications such as PCs, mobile phones, tablets, electric vehicles, data centers, robots, smart manufacturing and industrial control, and autonomous vehicles. Every new application can be transformed from concept to practice, relying on the efforts of innovators and the support and collaboration of a complete supply chain system. Today, artificial intelligence technology has once again brought new development opportunities. AI has moved from the layout of cloud giants to the enterprise and industrial manufacturing fields, and has penetrated into daily life. Under this trend, EDOM is optimistic about four major areas:Power and cooling technologies: With the rapid growth of AI computing requirements, high-efficiency power management and cooling technology will become an important foundation to support the operation of future electronic products and data centers.Cybersecurity: In the era of digitalization and the Internet of Everything, the importance of information security protection continues to increase, and cybersecurity will become an indispensable part of the stable operation of enterprises.Optical communication: Optical communication and co-packaged optics (CPO) technology are regarded as important keys to breaking through the bottleneck of AI computing transmission. They will not only improve the operational efficiency of data centers, but also accelerate the development of edge computing and high-speed transmission applications.Integration of semiconductor technology and biomedicine: By combining technology and biomedicine, the automation of testing in medical institutions and laboratories can be further promoted, the process of precision medicine can be accelerated, and key breakthroughs can be brought to life sciences.Wayne Tseng, Chairman of EDOM Technology, said: "In the torrent of technological change, our vision of making the world a better place through innovation has remained steadfast. We are deeply honored to witness and participate in the development and evolution of the electronics industry. We sincerely thank the vendors, customers, and partners who have worked side by side with us over the past thirty years, and we look forward to continuing to work together in leading the next generation of technological innovation in the future."With thirty years of evolution, EDOM resonates with the pulse of the global electronics industry. Facing the ever-changing technological wave, we always keep abreast of the latest trends, enrich the electronics industry ecosystem, and open chapters of innovation with global partners.

On the opening day of COMPUTEX 2026, AIC Inc. hosted a high-level strategic panel session at its booth, focusing on overcoming the "memory wall" challenge. Industry giants and key strategic partners, including NVIDIA and VAST Data, joined AIC for a presentation on their latest platforms designed to eliminate bottlenecks in Large Language Model (LLM) inference and intensive AI workloads, marking a critical evolution in active AI storage driven by Agentic AI in 2026.In his opening remarks, Michael Liang, CEO and President of AIC, outlined the new challenges facing AI infrastructure as AI applications enter the "Long Context" era. The transition to long-context and Agentic AI has completely shifted the primary AI infrastructure bottleneck from raw computational speed to massive data movement and memory bandwidth constraints—a hurdle commonly known as the "Memory Wall."Liang emphasized that the shift toward autonomous AI agents executing task decomposition and multi-step APIs is fundamentally transforming data center demands and reshaping underlying AI infrastructure. Consequently, AIC is actively collaborating with NVIDIA and VAST Data to develop advanced, AI-native storage solutions. By integrating the NVIDIA Vera BlueField-4 STX Storage Processor into its hardware platforms, AIC is building the essential infrastructure required to eliminate bottlenecks and accelerate workloads for Agentic AI applications.NVIDIA Ecosystem Scales Agentic AI Adoption WorldwideJason Hardy, NVIDIA's Vice President of Storage Technology, highlighted the significance of "Agentic Inferencing", a key theme from the NVIDIA GTC Taipei keynote during COMPUTEX 2026. Agentic AI requires more than faster compute; it demands fast, secure access to context memory so agents can reason across long sessions, large datasets, and complex workflows.NVIDIA Vera BlueField-4 STX addresses this paradigm shift. It enables a new class of AI-native storage infrastructure for context memory, built with Vera-based BlueField-4, NVIDIA Spectrum-X Ethernet, NVIDIA DOCA, NVIDIA Dynamo, and NVIDIA AI Enterprise. This foundation provides NVIDIA's storage partners, such as AIC, with the essential building blocks to keep agent context and inference data close to the compute path, significantly improving throughput, responsiveness, and infrastructure efficiency.NVIDIA is actively building a robust partner ecosystem around the NVIDIA Vera BlueField-4 STX architecture, spanning storage, systems, cloud infrastructure, and security sectors. Key partners like AIC are collaborating closely with NVIDIA to integrate, validate, and bring this next-generation infrastructure to market. Hardy emphasized that these close alliances will help customers optimize resource utilization, reduce costs, accelerate response times, and enhance security during large-scale deployments, thereby ushering in the era of Agentic Inferencing.VAST Data and AIC Hard-Soft Integration Optimizes AI InfrastructureEchoing the new design of NVIDIA Vera BlueField-4 STX, VAST Data CTO Andy Pernsteiner emphasized that Agentic AI requires sophisticated mechanisms for managing and optimizing massive-scale KV caching to persistent memory. This avoids redundant, expensive prefill computations across multi-turn, long-context sessions, while providing new storage platforms that support confidential computing and data protection for highly sensitive information. VAST Data integrates seamlessly with NVIDIA's BlueField-4 DPU architectures and Dynamo routing frameworks to offload, share, and reuse KV cache context across wide GPU clusters.The strategic hardware-software partnership between VAST Data and AIC pairs AIC's advanced server hardware with VAST's software intelligence to build next-generation AI infrastructure and context memory storage platforms. Integrating NVIDIA Context Memory Storage (CMX) platform, featuring the NVIDIA Vera BlueField-4 STX storage processor, effectively resolves GPU KV cache bottlenecks. By utilizing fast NVMe arrays as a shared, high-bandwidth context tier, the solution significantly increases tokens-per-second throughput and energy efficiency for long-context, multi-turn AI inferencing.AIC Embraces NVIDIA Vera BlueField-4 STX for Agentic AIAs Liang stated in a post-event interview with DIGITIMES, the company has successfully built its storage server business since 2014. By continuously reinvesting 15% of its annual revenue every year into R&D and early-stage development of new architectures, AIC has positioned itself as a key player in developing next-generation, Agentic AI-native storage infrastructure.Today, AIC is established as a Solution Advisor in the NVIDIA Partner Network (NPN). AIC also is building upon a strategic partnership with VAST Data that began seven and a half years ago. To meet the surging demand for AI data centers, AIC's strategic expansion in Yangmei, Taiwan, and Haiphong, Vietnam, directly targets the skyrocketing global demand for artificial intelligence data centers. These state-of-the-art manufacturing footprints allow the company to scale production of AI servers and high-density storage while seamlessly integrating computing, networking, and security into unified infrastructure platforms.The new facilities anchor AIC's global supply chain and position the company to meet the intense deployment needs of cloud service providers and enterprise customers. This empowers customers to maintain a competitive lead and achieve greater success amidst the AI wave.

The megatrend in electronic design today is end-to-end collaboration across ICs, packaging, PCBs, systems, data centers, and physical applications, with rapidly evolving artificial intelligence playing an increasingly critical role.In early June, Graser Technology held its annual technology forum, Graser TECHTALKS 2026, under the theme "AI in Sync: Intelligent Design, Accelerated Manufacturing." The event focused on how AI connects design, analysis, and manufacturing workflows. It brought together industry speakers, in-house engineering experts, and customer representatives to share professional insights and real-world experience, outlining a new paradigm for electronic design workflows and industrial applications in the AI era.In her opening remarks, Graser Chairwoman Lillian Pan said the company has, for more than 30 years, upheld the principles of fast response, professional service, and long-term partnership, helping customers turn ideas into products faster. She added that Graser will continue promoting the leverage of AI across engineering workflows, introducing advanced design tools, and supporting Taiwan's semiconductor and electronics industries in remaining globally competitive.AI as a Design Workflow CollaboratorIn the first keynote, "Paradigm Shift of System Design in the AI Era," Michael Shih, Corporate Vice President for APAC and Japan at Cadence, said electronic design is facing a new level of complexity as Moore's Law becomes harder to sustain and the cost of advanced process technologies and system integration continues to rise.He noted that the challenge is no longer limited to designing a single chip. Instead, engineering teams must increasingly solve complex issues across chips, advanced packaging, PCBs, system-level design, and multiple physical domains. Against this backdrop, Cadence has been expanding its focus from IC design into packaging, PCB design, multiphysics simulation, data centers, and system analysis, evolving from a traditional EDA tool provider into an Intelligent System Design platform company.Shih explained that Cadence's Intelligent System Design platform brings together AI, EDA and IP, system design and analysis, and computational software. This enables engineering teams to perform simulation, analysis, optimization, and design verification at the system level. Within this framework, Cadence is pursuing AI in two directions: Design for AI, which helps customers build AI infrastructure, and AI for Design, which embeds AI directly into design solutions. In other words, AI is not only an application enabled by advanced ICs and systems; it is also becoming a core collaborative capability within the electronic design process.A major part of this shift is the introduction of agentic AI into design workflows. Shih said Cadence is bringing AI agents into front-end design and verification, digital implementation, and custom and analog design processes.These AI agents can help engineers understand design goals, break down tasks, execute workflows, and accelerate iterative design cycles. Their value goes beyond labor savings: by automating repetitive and time-consuming work, AI agents allow design teams to explore feasible options faster, shorten development cycles, and reduce the time and cost pressures created by rising complexity of designs.Shih noted that, for example, many companies must complete large numbers of board designs every year, involving repetitive yet expertise-intensive tasks such as placement, routing, layout, and design checks. By introducing AI into these workflows, engineers can spend more time on system architecture, reliability, and innovation. This suggests that design automation in the AI era is moving beyond point-tool acceleration toward broader efficiency gains across ICs, packaging, PCBs, and system-level simulation.AI Deployment Through System IntegrationFocusing on system integration design trends in the AI era, Eric Kao, Business Development Director at Giga Computing, shared his perspective from the data center infrastructure side. He noted that as enterprises adopt AI agents and generative AI applications, inference workloads are growing rapidly, pushing data center architectures originally optimized for AI training to shift.This shift is also redefining the role of the CPU. Because AI agent workflows involve task decomposition, step-by-step planning, API calls, tool invocations, and other logic-heavy and I/O-intensive operations, the CPU is no longer just a supporting component next to GPUs or accelerators. Instead, it is becoming the control and orchestration hub inside the AI data center.Kao pointed out that future AI infrastructure will move toward more refined heterogeneous computing configurations. Effectively managing different platforms and resources—and matching the right hardware to the right models and workloads—will become a critical system design challenge.Giga Computing's own technology roadmap also reflects this transition. According to Kao, the company has expanded from server motherboards and system development into HPC, OCP, GPU servers, liquid cooling, heterogeneous computing platforms, and broader AI infrastructure services. This shows that competition in AI data centers is shifting from standalone server specifications to integrated capabilities across racks, cooling, networking, software, POD design, and system-level simulation.Po-Ting Lin, Professor in the Department of Mechanical Engineering and Director of the Center for Intelligent Robotics (CIR) at National Taiwan University of Science and Technology (NTUST), approached AI from the perspective of physical system applications. He shared his team's experience applying AI to obstacle-avoiding path planning for robotics.Lin explained that when a robot encounters nearby people or obstacles during operation, it must quickly determine a safe trajectory to avoid collisions. Traditional optimization methods can be used to search for safe paths, but they often require significant computation time. By incorporating AI models, the system has the potential to greatly shorten response time.Lin emphasized that robot obstacle avoidance is not about taking the longest possible detour. The goal is to find a path that avoids obstacles just enough while maintaining task efficiency. NTUST's robotics research covers human-robot collaborative robotic arms, UAV inspection, and dual-arm robotic systems, with a common focus on balancing safety and operational efficiency.Through the insights shared by these two speakers, it is evident that bringing AI into real-world applications depends not only on a single chip or algorithm but also on the integration of computing, software, sensing, simulation, and physical systems.Intelligent Tools and Simulation Integration Across the Design FlowThe afternoon sessions of Graser TECHTALKS 2026 focused on two major tracks: electronic system design automation and multiphysics simulation. Graser's engineering team highlighted the latest advances in Cadence Allegro/OrCAD X 25.1 and Allegro X AI, demonstrating how automation and AI-assisted design can improve PCB development workflows.The program also featured technical experts from AIC, Supermicro, and Cadence, who shared practical insights into power integrity, electrothermal co-simulation, AI server system design, and multiphysics optimization, spanning packaging to system-level design, using Cadence Sigrity, Clarity, Celsius 3D, Sigrity HPC, and Aurora.Graser also presented updates to its in-house software portfolio, including GraserWARE, GIMS, and CAMPro, addressing requirements such as circuit reliability checks, component and BOM management, and manufacturing data validation.Building on features introduced last year, the company added several practical tools to GraserWARE MSAPack, including simulation schedule management, stackup format conversion, S-parameter port-naming optimization, temperature-dependent material parameter fitting, and automatic Power Tree generation. These capabilities help streamline SI/PI simulation workflows while improving analysis efficiency and data consistency.The key takeaway from Graser TECHTALKS 2026 is that in the AI era, design competitiveness goes beyond upgrading individual tools—it depends on how effectively organizations can synchronize design, analysis, verification, and manufacturing data to enable faster, more agile system-level development.