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

DATOTEK INTERNATIONAL CO., LTD., a Taiwan-based memory and storage manufacturer, showcased its latest AI-powered sustainable storage solutions and AI infrastructure technologies at COMPUTEX 2026 in Taipei under the exhibition theme "AI Together." The company presented a comprehensive portfolio of AI storage, edge AI computing, PCIe Gen5 SSD thermal management, USB4 portable SSDs, and next-generation memory products designed for both enterprise and consumer markets

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

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.