j5create, a global leader in 3C peripherals and smart workspace solutions, is showcasing a comprehensive lineup of new smart connectivity products at COMPUTEX 2026. Focusing on cross-device collaboration, wireless audio/video (AV), smart desktops, and AI-integrated applications, the exhibition demonstrates how j5create is expanding beyond traditional connectivity hardware to deliver a more intuitive, unconstrained digital experience

PEGATRON Corporation (PEGATRON) officially opened its booth at COMPUTEX 2026, presenting its AI Tech Maker strategy and its role in helping move AI from infrastructure planning to real-world deployment

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

As global manufacturing accelerates its smart-factory transition, computer vision is taking on an increasingly critical role in industrial quality inspection. Canadian AI startup DaoAI, on the strength of its innovative AI vision technology, has secured partnerships with international heavyweights including Siemens and BASF. Co-founder and CTO Xiaochuan Chen explains how DaoAI uses "Feature Cognition Inspection" to solve the high false-call rates and time-consuming programming pain points of traditional Automated Optical Inspection (AOI), and reveals plans to actively pursue deeper partnerships with Taiwanese equipment makers and distributors during COMPUTEX 2026.From Academic Research to Industrial Practice: Bringing AI to the Electronics Manufacturing FloorDaoAI CTO Xiaochuan Chen has been working in AI and vision research in Canada since 2014 - right at the inflection point of deep learning. In 2017, he co-founded DaoAI in Vancouver alongside a partner with a track record of successful entrepreneurship, leading a top-tier AI vision team drawn from University of British and University of Waterloo and focused squarely on industrial automation.Chen sees enormous potential for AI in manufacturing across both North American and Asian markets. DaoAI's technology not only lifts production yield but also protects enterprise data sovereignty through its on-premise data architecture. "We understand that in any digital transformation, the security and ownership of data is a core interest for manufacturers - and that's the foundation our technology is built on," Chen says.Solving the Long Programming Cycles and High False-Call Rates of Traditional AOITraditional AOI algorithms running on PCBA (printed circuit board assembly) inspection lines are notorious for high false-call rates. Chen explains that conventional algorithms rely heavily on color matching or pixel-level comparison - when, for example, a resistor and the board substrate are both black, traditional algorithms struggle to tell them apart.DaoAI's core technology is Feature Cognition Inspection. The model is pretrained on a dataset of more than one million images, abstracting what the AI sees into a specialized feature space. The advantages show up at two levels: 1. Multi-dimensional differentiation: the AI no longer compares colors - it precisely distinguishes whether a defect is present on a component within feature space. 2. Continuous learning: the system mirrors how humans learn. If the AI gets a call wrong on the first pass, the inspector's feedback is fed into its memory system, so the next time a similar component appears, the same mistake doesn't recur."We pretrain a PCBA-specific inspection model on real production-line data," Chen explains. "All the customer needs is a single 'reference board.' Without any CAD file or component library, the AI identifies the location of every component, automatically generates inspection regions, and automatically calculates thresholds. Programming can be done in seconds or minutes - the AI takes over the part of AOI that historically required the most human intervention."This kind of fast programming is especially well-suited to high-mix, low-volume production. It dissolves the bottleneck that NPI (new product introduction) phases used to hit, where modeling was slow and dependent on dedicated programming engineers.Solving the Compute-and-Sovereignty Trade-off Without Cloud DependenceFor data sovereignty and information security issues that customers care about deeply - DaoAI runs 100% on-premise. To deliver high performance within the limited compute budget of edge hardware, DaoAI takes a "pretraining + rapid fine-tuning" approach: customers run a pre-tuned, optimized specialty model locally while keeping their data fully secure.Cross-Border Partnerships and the COMPUTEX Strategy: Complementing Taiwan's Supply ChainDaoAI has already established deep partnerships with Siemens (electronics manufacturing and automation platform integration) and BASF (vision analysis applications in chemicals). Looking ahead, Chen is bullish on the Taiwan market and announced that DaoAI will participate in COMPUTEX for the first time this year.DaoAI positions itself as a vision-AI application company, Chen says, and the trip to Taiwan has two strategic objectives: 1. Hardware integration: partner with local Taiwanese equipment manufacturers to combine DaoAI's AI software algorithms with Taiwan's high-quality hardware, delivering customized solutions. 2. Distribution expansion: identify professional distributors and service partners in Taiwan to get closer to local electronics manufacturing customers.Beyond Surface Mount Technology line inspection, DaoAI is also strongly interested in semiconductor packaging and testing and is looking to co-develop new applications with Taiwanese probe and inspection equipment makers - pushing the boundaries of vision AI further still.

As Low Earth Orbit (LEO) satellite competition intensifies, satellite communication has evolved from a terrestrial supplement into a core infrastructure for AI and cloud services. Emtar Technologies, a Canadian chip design startup founded by Taiwanese-Canadian entrepreneur Alvis Huang, is emerging as a critical player in this shift. Leveraging his background as a Marconi Young Scholar, Huang has led Emtar to develop groundbreaking 6G Non-Terrestrial Network (NTN) chips that have already garnered support from TSMC and the Canadian government.Breakthrough Performance: The "Private Library" ArchitectureAt the recent TSMC North America Technology Symposium, Emtar conducted a Live Demo of its 6G NTN solutions, demonstrating high strategic value to the semiconductor supply chain. Emtar's chipset - comprising high-performance RF Front-End ICs and Intelligent Beamforming chips - acts as the system's "sensory organs and brain."Unlike traditional architectures where transceivers must "queue" to access shared memory (SRAM), Emtar utilizes a disruptive fully embedded memory design paired with proprietary algorithms. This gives each transceiver a "private library" for instantaneous data scheduling, resulting in: 1. 10x faster tracking and position prediction. 2. 2x higher reception sensitivity. 3. Significant power savings (dozens of watts), solving critical heat dissipation issues for satellite equipment.National Recognition and Global ExpansionEmtar's strategic importance is backed by high-level Canadian endorsement. Emtar participated in a Canadian trade mission to South Korea led by Minister Maninder Sidhu, where the company engaged with potential satellite industry partners. Additionally, Emtar was named "Startup of the Year" by Canada's Semiconductor Council (CSC), an organization featuring industry titans like AMD, Intel, and Qualcomm.The Future: Data Centers in SpaceHuang anticipates the LEO market will reach 2 billion users within seven years, driven by "Data Centers in Space." Cloud providers are eyeing orbital AI deployments to mitigate terrestrial geopolitical risks. This shift demands high-efficiency satellite access for everything from autonomous drones to maritime vessels - these are Emtar's primary target market.With products entering mass production by year-end, Emtar is currently engaging with Taiwanese ODMs and space agencies during COMPUTEX Taipei. As products head toward mass production by the end of the year, Emtar plans to launch its Series A funding round. Huang emphasized that he is looking for investors with Silicon Valley experience who can provide top-down strategic resources to help Emtar connect with tier-one global satellite operators. From a Canadian startup to an industry star, Emtar is poised to leave its mark on the 6G space race, blending Taiwanese entrepreneurial resilience with North American technical innovation.

As the global technology industry descends on Taipei for COMPUTEX 2026 this June, a Canadian startup is preparing to challenge the fundamental architecture of modern processing. Irreversible, a Montreal-based firm with deep roots in quantum computing, is unveiling a "physics-first" analog in-memory computing architecture that promises a staggering 1,000x reduction in power consumption compared to conventional digital processors.Unlike many silicon startups that originate in traditional chip design, Irreversible’s journey began in the highly constrained world of quantum physics. The core team originally specialized in quantum computing, where they were forced to solve computing problems within the extreme limitations of a dilution refrigerator - an environment where even the slightest heat or noise can destroy a fragile quantum state. Co-Founder Dominic Marchand explains that this background led them to become a "computing company that found its way to designing chips," rather than the reverse. This unique DNA pushed the team to strip away decades of architectural abstractions and return to the most basic laws of physics to find the most energy-efficient ways to process information.The current industry obsession with massive Large Language Models (LLMs) has created a significant energy crisis, particularly at the "extreme edge" where devices must operate on microwatt-class power. Irreversible addresses this by sidestepping the Von Neumann bottleneck, the energy-intensive movement of data back and forth between memory and the processor. By performing calculations directly in memory and maintaining a fully analog signal path, Irreversible also eliminates the power-hungry digital-to-analog conversions that often limit hybrid AI chips. This approach recognizes that while digital logic offers noise protection, the energy required to strictly maintain ones and zeros is a luxury that edge sensors can no longer afford.A critical point of differentiation is how Irreversible compares to other innovators in this space. Marchand notes that while he is proud of the Canadian leadership in analog in-memory compute, Irreversible maintains several distinct advantages. First, the company is memory-agnostic, meaning they are not tied to a single proprietary memory technology and can instead utilize various non-volatile memories and emerging RRAM roadmaps. Second, the company places extraordinary emphasis on its software and simulation tools, which allow their hardware and software teams to work in lockstep. Their proprietary hardware-aware training ensures that neural networks remain accurate by accounting for the inherent variability of analog circuits during the initial training phase.For their up-coming visit to Taipei, Irreversible has set clear strategic objectives to integrate with the world's leading semiconductor ecosystem. A primary goal is establishing high-level connections with semiconductor foundries to gain privileged access to specific memory cells, which are essential for their "physics-first" custom designs. Additionally, the company is actively seeking partnerships with OEMs and solution integrators. By bringing intelligence directly to the sensor site, Irreversible aims to enable "previously impossible" use cases, such as deploying sophisticated AI on small drones or always-on wearable devices that cannot support a traditional GPU. Ultimately, Irreversible arrives in Taipei not just to showcase a chip, but to advocate for a shift in how the world thinks about intelligence. By trading the rigid certainty of digital bits for the natural efficiency of physics, they are proving that the future of AI isn't just about more power - it's about more efficient computing.