As the AI computing race accelerates and semiconductor manufacturing capacity is increasingly redirected toward advanced memory, the memory industry is shifting from price-driven competition to a battle over resource allocation and supply security. The rapid expansion of high-bandwidth memory (HBM) production continues to consume advanced fabrication capacity, constraining the supply of conventional memory products and creating persistent shortages across the market. This concentration of high-end production has widened the supply-demand gap, driving the average selling prices of certain premium memory products to levels eight to ten times higher than previous norms

Rugged edge computing brand – Cincoze will showcase its latest embedded computing solutions at COMPUTEX Taipei 2026, from June 2–5 (Hall 2, Booth Q0401a). Cincoze will display its full range of edge computing solutions around the theme of "Smarter, Faster, More Reliable - Powered by Edge AI" encompassing the full spectrum of edge application environments in four dedicated zones, including DIN-Rail computers, industrial panel PCs and monitors, rugged embedded computers, and GPU embedded computers

Technology news, product launches and show highlights from the event attendees available for download (PDF).

"Integrated circuit" is a fancy term for the chips that control the electronics that make our modern life possible. They are composed of hundreds of thousands or even millions of tiny components, such as transistors, capacitors, and resistors. These components are so tightly linked together on a tiny piece of silicon that, for all intents and purposes, the chip is indivisible – you can't separate it into its individual components.Any mistake in a chip's circuitry could thus render the entire chip unusable. The process of transferring the as-yet theoretical chip design to the fabrication plant (also known as a foundry or chip fab) to produce a photomask (the template that will later be used to create chips en masse) is known as the tape-out, and costs in the tens of millions of dollars.So chip designers have a strong financial incentive to get their designs right on paper before any silicon is involved.But research conducted for Siemens in 2024 showed that only 14 per cent of chips experienced what is called first-silicon success – when the first tape-out results in a photomask and a physical chip that reliably works. This research also noted that this was the lowest success rate over the past two decades. One reason for that might be that many companies are now bringing chip design in-house instead of contracting out to experienced, specialised chip designers.That's where Vincent Bligny's company, Aniah, steps in.Based in Grenoble in the French Alps, Aniah uses artificial intelligence to conduct electrical rule checks (ERCs) for integrated circuits. These checks examine the electrical connections between different components and verify that the chip design can be made, will work, and will continue to work over time. For example, ERCs look for short circuits, when current flows with very little resistance between two components that it was not supposed to flow between; and they look for open circuits, the opposite of a short circuit, where the circuit is not a closed loop and current therefore cannot flow.ERCs also look for large voltage drops (often because of high resistance in the circuit), leading to overheating, noise, and reduced efficiency; and they look for voltage violations, where two components operating at different voltages are connected, potentially damaging the chip (level shifters are necessary to get around this problem). Traditional checkers, such as the industry-standard simulator SPICE (Simulation Program with Integrated Circuit Emphasis), examine how the chip might behave under different voltage, current, or temperature conditions. However, they are unable to sample every potential configuration of the circuit’s components and might miss corner cases (the rare cases where the parameters are at the limits of their expected range). Aniah's OneCheck, on the other hand, covers all of these potential configurations. If there are N components that can take on either high or law values (e.g., N power domains that can be switched on or off), OneCheck will examine each of the 2N potential states to make sure that the chip will still run correctly. It will go over all possibilities, catching problems such as conditional high-impedance (floating) nodes and electrical-overstress violations in chips that mix high- and low-voltage domains. Unlike traditional checkers, OneCheck doesn't depend on luck: it is a static, simulation-free method. Moreover, traditional checkers might find the first instance of a particular type of violation and immediately report back that this violation is present. The designer might fix this one violation, but many other instances of this violation could still remain. OneCheck, on the other hand, will look for all instances of the violation, so that the designer can fix them all at once and not have to rerun the checker.Not only is OneCheck more comprehensive than traditional electrical rule checkers but it is also faster, giving results in minutes instead of days. There is a trade-off, of course: OneCheck finds more false positives than traditional checkers. In other words, it flags more violations that really aren’t violations at all. However, this is something that most chip designers can accept, especially because of another product that Aniah released earlier this year, Amigo.Amigo is an AI agent that goes hand-in-hand with OneCheck. OneCheck flags potential violations and groups them into roughly fifty clusters, organised by their root cause. Amigo then provides users with an explanation of these violations, and takes advantage of natural language processing – users can simply enter "Explain this violation" – to allow users to query and thus debug these violations. But Amigo's support for chip designers goes further than that. It also provides concrete suggestions for how to fix the violations that it has identified, which the chip designer can then validate. It also tells chip designers which violations are the 'lowest-hanging fruit,' whose correction would lead to the greatest increase in the likelihood that the chip will be ready for tape-out. In fact, Amigo quantifies for users just how ready a design is for tape-out, and can quickly adjust this valuation when changes are made (instead of running the entire checking process from scratch).OneCheck and Amigo are therefore a newer, AI-enabled system for electronic design automation (EDA), and Aniah has ambitious plans for developing this EDA technology over the next two years.  They intend to implement parallel fixing by the end of 2026, where the checker can make corrections as it checks. This is a prelude to delivering results in just seconds by next year.Aniah next wants to go beyond OneCheck and simply electrical rule checking to full circuit sign-off – in other words, it wants to be able to verify that physical design constraints are respected. In the long term, Aniah wants to enable continuous sign-off, whereby all electrical rules and physical constraints are constantly satisfied. Currently, sign-off is a hectic scramble at the end of the design process, where bugs are easy to overlook.Their bronze award at the 2026 Best AI Awards came with a prize of NTD 500,000 (USD ~$16,000). The money itself won't help Aniah a lot – they've already raised more than USD $11 million in funding and expect to open a new funding round soon – but the award will open doors with Taiwanese companies and investors, said Allen Chen, Aniah's director of applications engineering. Chen, who is based in Taipei, added that Bligny, Aniah's CEO, has long held Taiwan's technical prowess in semiconductor manufacturing – its world-leading foundries and design houses – in high esteem. This is why Bligny plans on expanding their Taiwanese team, said Chen, who joined the company three years ago and is currently Aniah’s only employee on the island.Aniah currently has one Taiwanese customer, Novatek, but also supports the 200-strong Taiwanese team of Nvidia, an American company. These clients are loyal to Aniah because OneCheck and Amigo offer the fastest running time and the smallest number of false positive alerts, and because Aniah is quickly adopting AI to improve its service.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.

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.