EDA Pushes Deeper Into AI


EDA vendors are ramping up the use of AI/ML in their tools to help chipmakers and systems companies differentiate their products. In some cases, that means using AI to design AI chips, where the number and breadth of features and potential problems is exploding. What remains to be seen is how well these AI-designed chips behave over time, and where exactly AI benefits design teams. And all o... » read more

Improving AI Productivity With AI


AI is showing up or proposed for nearly all aspects of chip design, but it also can be used to improve the performance of AI chips and to make engineers more productive earlier in the design process. Matt Graham, product management group director at Cadence, talks with Semiconductor Engineering about the role of AI in identifying patterns that are too complex for the human brain to grasp, how t... » read more

LLM Inference On CPUs (Intel)


A technical paper titled “Efficient LLM Inference on CPUs” was published by researchers at Intel. Abstract: "Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging due to the astronomical amount of model parameters, which requires a demand for large memory capacity an... » read more

Applications Of Large Language Models For Industrial Chip Design (NVIDIA)


A technical paper titled “ChipNeMo: Domain-Adapted LLMs for Chip Design” was published by researchers at NVIDIA. Abstract: "ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: custom tokenizers, domain-ad... » read more

Unlocking The Power Of Edge Computing With Large Language Models


In recent years, Large Language Models (LLMs) have revolutionized the field of artificial intelligence, transforming how we interact with devices and the possibilities of what machines can achieve. These models have demonstrated remarkable natural language understanding and generation abilities, making them indispensable for various applications. However, LLMs are incredibly resource-intensi... » read more

LLMs For Hardware Design Verification


A technical paper titled “LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation” was published by researchers at University of Cambridge, lowRISC, and Imperial College London. Abstract: "Test stimuli generation has been a crucial but labor-intensive task in hardware design verification. In this paper, we revolutionize this process by harnessing the power of large langua... » read more

LLM-Aided AI Accelerator Design Automation (Georgia Tech)


A technical paper titled “GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models” was published by researchers at Georgia Institute of Technology. Abstract: "The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have dramatically escalated the imperative for specialized AI accelerators. Nonetheless, designing these accele... » read more

LLM-Assisted Generation Of Formal Verification Testbenches: RTL to SVA (Princeton)


A technical paper titled “From RTL to SVA: LLM-assisted generation of Formal Verification Testbenches” was published by researchers at Princeton University. Abstract: "Formal property verification (FPV) has existed for decades and has been shown to be effective at finding intricate RTL bugs. However, formal properties, such as those written as System Verilog Assertions (SVA), are time-con... » read more

A Chiplet-Based Supercomputer For Generative LLMs That Optimizes Total Cost of Ownership


A technical paper titled "Chiplet Cloud: Building AI Supercomputers for Serving Large Generative Language Models" was published by researchers at University of Washington and University of Sydney. Abstract: "Large language models (LLMs) such as ChatGPT have demonstrated unprecedented capabilities in multiple AI tasks. However, hardware inefficiencies have become a significant factor limiting ... » read more

Generative AI Training With HBM3 Memory


One of the biggest, most talked about application drivers of hardware requirements today is the rise of Large Language Models (LLMs) and the generative AI which they make possible.  The most well-known example of generative AI right now is, of course, ChatGPT. ChatGPT’s large language model for GPT-3 utilizes 175 billion parameters. Fourth generation GPT-4 will reportedly boost the number of... » read more

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