Can AI Create Missing Models?


Key takeaways Models are an essential part of EDA flows, each capturing necessary detail while retaining good execution performance. Models have been expensive to create, maintain and verify, restricting their utilization, but AI may be able to significantly reduce their cost. A deeper question remains. Should AI be used to create models that help existing flows, or should AI be used... » read more

DeepSeek’s New AI Models: V3.2 and V3.2-Speciale


DeepSeek published two new AI models: V3.2: Pushing the Frontier Of Open LLMs. The company claims the model "performs comparably to GPT-5." and V3.2-Speciale. DeepSeek claims the model "surpasses GPT-5 and exhibits reasoning proficiency on par with Gemini-3.0-Pro." Find the technical paper here and here.  "DeepSeek-V3.2 is our first model to integrate thinking directly into tool-us... » read more

Overflowing Zoo: The Power Of Compilers


The term “model zoo” first gained prominence in the world of Artificial Intelligence/Machine Learning (AI/ML) beginning in the 2016-2017 timeframe. Originally used to describe open-source public repositories of working AI models — the most prominent of which today is Hugging Face — the term has since been adopted by nearly all vendors of AI chips and licensable Neural Processors Units (... » read more

Speeding Time To Market With A Future-Proof Fabric


This whitepaper covers how Tenstorrent is elevating their AI fabric to new heights of performance, efficiency, and productivity through a collaboration with Baya Systems. Tenstorrent’s in-house fabric has set a new standard for efficiency and performance in AI compute in their current generation products and is proactively addressing the needs of the next generation. By combining Tenstorrent�... » read more

Balancing Workloads In AI Processor Designs


A growing number of AI processors are being designed around specific workloads rather than standardized benchmarks, optimizing performance and power efficiency, but often with enough flexibility to adapt to future changes. While the fundamentals of matrix multiplication and software optimization still apply, those alone are no longer sufficient. Designs need to address specific data types, w... » read more

Reliable Training Data Paramount To AI Model Success


AI systems are increasingly being integrated into safety- and mission-critical applications ranging from automotive to health care and industrial IoT, stepping up the need for training data that is reliable, secure, and which is generated from trusted sources. AI activity is growing exponentially, as everybody tries to figure out how to apply it to their domain, application, or workload. In ... » read more

Security Tradeoffs: A Difficult Balance


Experts At The Table: Semiconductor Engineering sat down to discuss hardware security challenges, including new threat models from AI-based attacks, with Nicole Fern, principal security analyst at Keysight; Serge Leef, AI-For-Silicon strategist at Microsoft; Scott Best, senior director for silicon security products at Rambus; Lee Harrison, director of Tessent Automotive IC Solutions at Sieme... » read more

Report: The AI Efficiency Boom


Artificial Intelligence (AI) is undergoing a fundamental transformation. While early AI models were large, compute-heavy, and dependent on cloud processing, a new wave of efficiency-driven innovations is moving AI inference—the generation of model results—to the edge. Smaller models, improved memory and compute performance, and the need for privacy, low latency, and energy efficiency are dr... » read more

AI: A New Tool For Hackers, And For Preventing Attacks


Semiconductor Engineering sat down to discuss hardware security challenges, including new threat models from AI-based attacks, with Nicole Fern, principal security analyst at Keysight; Serge Leef, AI-For-Silicon strategist at Microsoft; Scott Best, senior director for silicon security products at Rambus; Lee Harrison, director of Tessent Automotive IC Solutions at Siemens EDA; Mohit Arora, seni... » read more

Review Paper: Wafer-Scale Accelerators Versus GPUs (UC Riverside)


A new technical paper titled "Performance, efficiency, and cost analysis of wafer-scale AI accelerators vs. single-chip GPUs" was published by researchers at UC Riverside. "This review compares wafer-scale AI accelerators and single-chip GPUs, examining performance, energy efficiency, and cost in high-performance AI applications. It highlights enabling technologies like TSMC’s chip-on-wafe... » read more

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