Paradigms Of Large Language Model Applications In Functional Verification


This paper presents a comprehensive literature review for applying large language models (LLM) in multiple aspects of functional verification. Despite the promising advancements offered by this new technology, it is essential to be aware of the inherent limitations of LLMs, especially hallucination that may lead to incorrect predictions. To ensure the quality of LLM outputs, four safeguarding p... » read more

Efficient Streaming Language Models With Attention Sinks (MIT, Meta, CMU, NVIDIA)


A technical paper titled “Efficient Streaming Language Models with Attention Sinks” was published by researchers at Massachusetts Institute of Technology (MIT), Meta AI, Carnegie Mellon University (CMU), and NVIDIA. Abstract: "Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses tw... » read more

Generating And Evaluating HW Verification Assertions From Design Specifications Via Multi-LLMs


A technical paper titled “AssertLLM: Generating and Evaluating Hardware Verification Assertions from Design Specifications via Multi-LLMs” was published by researchers at Hong Kong University of Science and Technology. Abstract: "Assertion-based verification (ABV) is a critical method for ensuring design circuits comply with their architectural specifications, which are typically describe... » read more

LLM Inference on GPUs (Intel)


A technical paper titled “Efficient LLM inference solution on Intel GPU” was published by researchers at Intel Corporation. Abstract: "Transformer based Large Language Models (LLMs) have been widely used in many fields, and the efficiency of LLM inference becomes hot topic in real applications. However, LLMs are usually complicatedly designed in model structure with massive operations and... » read more

Training Large LLM Models With Billions To Trillion Parameters On ORNL’s Frontier Supercomputer


A technical paper titled “Optimizing Distributed Training on Frontier for Large Language Models” was published by researchers at Oak Ridge National Laboratory (ORNL) and Universite Paris-Saclay. Abstract: "Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning. Recent studies on loss scaling ... » read more

Is Transformer Fever Fading?


The hottest, buzziest thing bursts onto the scene and captures the attention of the business press and even the general public. Scads of articles and videos are published about The Hot Thing. And then, in the blink of an eye, the world’s attention shifts to the Next New Thing! Are we talking about the latest pop song that leads the Spotify streaming charts? Perhaps a new fashion trend that... » read more

Efficient LLM Inference With Limited Memory (Apple)


A technical paper titled “LLM in a flash: Efficient Large Language Model Inference with Limited Memory” was published by researchers at Apple. Abstract: "Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their intensive computational and memory requirements present challenges, especially for device... » read more

Modeling Compute In Memory With Biological Efficiency


The growing popularity of generative AI, which uses natural language to help users make sense of unstructured data, is forcing sweeping changes in how compute resources are designed and deployed. In a panel discussion on artificial intelligence at last week’s IEEE Electron Device Meeting, IBM’s Nicole Saulnier described it as a major breakthrough that should allow AI tools to assist huma... » read more

The Evolution Of Generative AI Up To The Model-Driven Era


Generative AI has become a buzzword in 2023 with the explosive proliferation of ChatGPT and large language models (LLMs). This brought about a debate about which is trained on the largest number of parameters. It also expanded awareness of the broader training of models for specific applications. Therefore, it is unsurprising that an association has developed between the term “Generative AI�... » read more

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

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