Foundation Model For Physics: The Next Layer Of Intelligence For Engineering


Over the past decade or so, foundation models have emerged as the dominant paradigm for interacting with language, images, and code. Large Language Models (LLMs) can generate text. Vision models can interpret images. Multimodal systems can connect the two seamlessly. But one domain has not yet seen the same foundation-model-level shift: validated, deterministic reasoning over the physical wo... » read more

Faster Verification Debug With AI


Every stage of semiconductor development takes longer and requires more effort with each new generation of chips. At no stage is this more apparent than functional verification. Industry consensus is that verification consumes roughly two-thirds of development time and resources. Within verification, debug is the most challenging step, consuming a third to two-thirds of the effort. Any serious ... » read more

Wafer-Scale vs. Chiplets: The New War? Part 1


Cerebras’ IPO is a meaningful moment for the semiconductor industry — and not just for the financial implications. Their confidence in their opening price reflects something the industry has effectively acknowledged: incremental chip scaling can no longer keep pace with what AI infrastructure demands. Radical approaches are earning serious consideration and serious capital. Cerebras... » read more

The Shape Of Prompts: Exploring Their Effect On Inference Infrastructure


AI inference prompts exhibit a shape-shifting behavior, arriving in many forms and attempting to fit themselves within the constraints of the inference stack. Ultimately, it is the design of the inference infrastructure that determines whether it can sustain a large volume of prompts or only a limited number. Prompts are not uniform transactions; they represent dynamic workload profiles whose ... » read more

Overcoming Bottlenecks In Data Movement


AI is all about data. There is more data to process, store, and move, and more tradeoffs required to do that efficiently and with enough flexibility to handle changes in future workloads. Nandan Nayampally, chief commercial officer at Baya Systems, talks about networks on chip and networks across chip, what the choke points are for data movement, and where and when data coherency makes sense. » read more

AI & Energy: Bending The Curve


By Pushkar P. Apte and Melissa Grupen-Shemansky Artificial intelligence (AI) is scaling at a pace that is reshaping semiconductor roadmaps, data center design, and long-term infrastructure strategy. AI promises many economic and social benefits, but the growth comes with an escalating demand for power, and energy has emerged as a major challenge. The AI & energy challenge AI training c... » read more

Enabling Production-Ready AI For Semiconductor Manufacturing


Semiconductor inspection has always been a scalability problem. Inspection teams are buried in manual reviews because the machines on the line throw false rejects, miss real defects, and can't learn from the data they're already producing. The job hasn't really changed in decades. Find defects faster. Find them with higher sensitivity. Keep cost down. And whatever you do, don't bury the review ... » read more

HW-Native, GPU Compiler for Large-scale ML Production Systems (UC San Diego, Meta)


A new technical paper, "TLX: Hardware-Native, Evolvable MIMW GPU Compiler for Large-scale Production Environments," was published by researchers at UC San Diego and Meta. Abstract "Modern GPUs increasingly rely on specialized hardware units and asynchronous coordination mechanisms, so performance depends on orchestrating data movement, tensor-core computation, and synchronization rather t... » read more

Why Vision LLMs Force A Rethink Of Edge AI Hardware


As vision-centric large language models move on-device, performance measured in raw TOPS is no longer enough. Architectures need to be built around real workloads, memory behavior, and sustained utilization, especially at the edge. Vision LLMs are changing the edge AI equation For the last decade, most edge AI silicon has been built to do one job extremely well: run convolutional networks for... » read more

SOCAMM2: Bringing LPDDR5X Benefits To AI Servers


The rapid scaling of artificial intelligence is reshaping nearly every dimension of data center design. While much of the focus has been on GPUs, accelerators and advanced packaging, another constraint is emerging as equally critical: power. As AI models grow larger and more complex, power consumption, not raw compute, is increasingly the limiting factor in system scalability. Modern AI work... » read more

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