By Anders Blom and Igor Markov“Force fields” have long captured our imagination — the invisible shields of science-fiction lore that protect starships and superheroes from harm. But in the world of scientific discovery, force fields play a much different role: They are mathematical models that let us peer into the atomic heart of matter itself.Now, thanks to breakthroughs in artif...» read more
Analog layout foundation modelResearchers from Pohang University of Science and Technology (POSTECH) built a foundation model for automated analog circuit layout.The team used a self-supervised learning approach, in which the model learns without human-provided labels. To counter a lack of available training data, the team divided analog layouts into small patches, masked part of each lay...» read more
The acceleration of technology is unprecedented: AI data centers, edge build-out, robotics, photonics, quantum, multi-die assemblies. Semiconductor Engineering Editor in Chief Ed Sperling talks with proteanTecs CEO Shai Cohen about what's changing and what impact it will have.Click here to listen.» read more
The semiconductor industry's shift toward chiplet-based architectures has created significant mixed-signal verification challenges for high-speed die-to-die interconnects. Traditional verification approaches force difficult trade-offs: Digital mixed-signal (DMS) flows sacrifice analog fidelity, while Analog mixed-signal (AMS) flows struggle with scalability and manual overhead. This paper detai...» read more
At a recent VLSI-D panel, industry leaders explored one of the most pressing topics in silicon design today — the intersection of AI-powered EDA, which is revolutionizing chip design for tomorrow.Ashish Darbari, CEO of Axiomise, questioned the panelists on the role of AI in chip design, optimizing PPA, validation and verification. While the panel explored the role of AI in design implemen...» read more
AI inference is no longer a single workload that can be served efficiently by a single type of accelerator or memory. From fast chat replies to 10M token codebases, inference spans wildly diverse workloads with very different limits on latency, bandwidth, capacity, and compute, as the figure below demonstrates.1Source: Meta1The AI inference spectrum of workloads includes: Inter...» read more
Artificial intelligence (AI) is stretching compute infrastructure well beyond what traditional enterprise data centers were designed to handle. Modern AI training requires massively parallel compute, low-latency networking, high-throughput storage pipelines, and facility engineering that can safely support higher rack power densities than legacy environments. These demands are fueling the eme...» read more
This blog post explains the cross-NUMA memory access issue that occurs when you run llama.cpp in Neoverse. It also introduces a proof-of-concept patch that addresses this issue and can provide up to a 55% performance increase for text generation when you run the llama3_Q4_0 model on the ZhuFeng Neoverse system.Cross-NUMA memory access problemIn llama.cpp, performance drops when the number o...» read more
Key Takeaways Extracting heat from a chip faster is a short-term fix to a bigger problem. The longer-term challenge is how to reduce the amount of energy used per query. Data movement, guardbanding, and software inefficiency are key targets for the future.Heat is a serious problem within AI chips, and it is limiting how much processing can be done. The solution is either to...» read more
The requirement to support real numbers in computers has existed for as long as computers themselves, yet has always been a more complicated challenge than it at first appears. Why? Because computer-based representations can only represent a finite subset of the continuum of real numbers. Consequently, they can only ever be considered an approximation – thereby demanding a diligent understand...» read more
Workflows and the addition of new capabilities are happening much faster than with previous technologies, and new grads may be vital in that transition.
Workflows and the addition of new capabilities are happening much faster than with previous technologies, and new grads may be vital in that transition.
Companies and governments invested heavily in onshoring fabs and facilities over the past 12 months as tariffs threatened to upset the global supply chain.
Memory chip shortages prompt price increases; Israeli chip foundry; 2 acquisitions; Baidu's AI chips; IBM's new quantum processor; GF's GaN push; 3D NAND scaling boosters; U.S. policy recommendations; +$500M in fundings; SiPho and SiGe capacity; EV joint venture.
Growing use cases include life science AI, reducing memory and I/O bottlenecks, data prepping, wireless networking, and as insurance for evolving protocols.
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