LLMs Show Promise In Secure IC Design


The introduction of large language models into the EDA flow could significantly reduce the time, effort, and cost of designing secure chips and systems, but they also could open the door to more sophisticated attacks. It's still early days for the use of LLMs in chip and system design. The technology is just beginning to be implemented, and there are numerous technical challenges that must b... » read more

In Memory, At Memory, Near Memory: What Would Goldilocks Choose?


The children’s fairy tale of ‘Goldilocks and the Three Bears’ describes the adventures of Goldi as she tries to choose among three choices for bedding, chairs, and bowls of porridge. One meal is “too hot,” the other “too cold,” and finally one is “just right.” If Goldi were faced with making architecture choices for AI processing in modern edge/device SoCs, she would also face... » read more

GDDR7 Memory Supercharges AI Inference


GDDR7 is the state-of-the-art graphics memory solution with a performance roadmap of up to 48 Gigatransfers per second (GT/s) and memory throughput of 192 GB/s per GDDR7 memory device. The next generation of GPUs and accelerators for AI inference will use GDDR7 memory to provide the memory bandwidth needed for these demanding workloads. AI is two applications: training and inference. With tr... » read more

Real-Time Low Light Video Enhancement Using Neural Networks On Mobile


Video conferencing is a ubiquitous tool for communication, especially for remote work and social interactions. However, it is not always a straightforward plug and play experience, as adjustments may be needed to ensure a good audio and video setup. Lighting is one such factor that can be tricky to get right. A nicely illuminated video feed looks presentable in a meeting, but on the other hand,... » read more

Mass Customization For AI Inference


Rising complexity in AI models and an explosion in the number and variety of networks is leaving chipmakers torn between fixed-function acceleration and more programmable accelerators, and creating some novel approaches that include some of both. By all accounts, a general-purpose approach to AI processing is not meeting the grade. General-purpose processors are exactly that. They're not des... » read more

Big Changes In Optical Inspection


Optical inspection always has been the workhorse technology for finding defects in chips. It's fast, cost-efficient, and generally reliable enough for most chips. But as logic scales into the angstrom range, and as systems become collections of chiplets, optical inspection needs to be combined with other types of inspection such as X-ray and acoustic. Kyle Vander Schaaf, application engineer at... » read more

ML Model Usage For Various Life Stages Of Semiconductor Test


By Shinji Hioki and Ken Butler From development through high volume manufacturing (HVM), semiconductor manufacturers’ pain points change based on the life stages. Each stage requires different types of applications to help with business needs. At the early stage, where the design and process are still immature, understanding the root causes of maverick material and implementing fixes is th... » read more

Survey: HW SW Co-Design Approaches Tailored to LLMs


A new technical paper titled "A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models" was published by researchers at Duke University and Johns Hopkins University. Abstract "The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language proce... » read more

Chip Industry Week In Review


Imec announced a new automotive chiplet consortium to evaluate which different architectures and packaging technologies are best for automotive applications. Initial members includes Arm, ASE, Cadence, Siemens, Synopsys, Bosch, BMW, Tenstorrent, Valeo, and SiliconAuto. Imec also launched star, a global network bringing together automotive and semiconductor innovators to address technological c... » read more

Paving The Way For Sustainable AI


Real-time requirements and the need for power-efficiency, security and privacy drives AI-processing at the edge. Key benefits of Edge AI include: -Low latency and real-time response -High power efficiency -Improved security and data privacy -Reduced cost A complementary set of AI-specific products and solutions, an end-to-end ML platform as well as an extensive application kno... » read more

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