The widespread adoption of Large Language Models (LLMs) is impeded by their demanding compute and memory resources. The first task of this paper is to explore optimization strategies to expedite LLMs, including quantization, pruning, and operation-level optimizations. One unique direction is to optimize LLM inference through novel software/hardware co-design methods. Given the accelerated LLMs, the second task of this paper is to study LLMs’ performance in the usage scenario of circuit design and verification. Specifically, we place a particular emphasis on functional verification. Through automated prompt engineering, we harness the capabilities of the established LLM, GPT-4, to generate High-Level Synthesis (HLS) designs with predefined errors based on 11 open-source synthesizable HLS benchmark suites. This dataset is a comprehensive collection of over 1000 function-level designs, and each of which is afflicted with up to 45 distinct combinations of defects injected into the source code. This dataset, named Chrysalis, expands upon what’s available in current HLS error models, offering a rich resource for training to improve how LLMs debug code. The dataset can be accessed at: https://github.com/UIUC-ChenLab/Chrysalis-HLS.

Large Language Models (LLMs) have revolutionized a wide range of applications with their strong human-like understanding and creativity. Due to the continuously growing model size and complexity, LLM training and deployment have shown significant challenges, which often results in extremely high computational and storage costs and energy consumption. In this paper, we discuss the recent advancements and research directions on (1) LLM algorithm-level acceleration, (2) LLM-hardware co-design for improved system efficiency, (3) LLM-to-accelerator compilation for customized LLM accelerators, and (4) LLM-aided design for HLS (High-Level Synthesis) functional verification. For each aspect, we present the background study, our proposed solutions, and future directions. An extended version of this work can be found at: https://arxiv.org/abs/2406.10903.