An Agile Framework for Efficient LLM Accelerator Development and Model Inference

Large Language Models (LLMs) have revolutionized many domains with exceptional performance while their large sizes hinder their broad applicability, especially in the edge computation scenarios. Designing large-scale LLM-specific accelerators is also challenging, suffering from the complicated, cumbersome, and time-consuming design, simulation, and optimization process. This paper meticulously proposes an agile framework for accelerator development, supporting efficient LLM inference. Firstly, we investigate the architecture of LLMs, uncover performance bottlenecks, and design an optimized binarized accelerator and a configurable RISC-V-based SoC to boost the inference of binary LLMs. Further, a novel fidelity-driven method is proposed to learn the multi-fidelity representation, solving the modeling and accuracy issues due to the lack of accurate later-stage data in the EDA flow, by capturing complex relationships among simulation metrics in and across different fidelities. Tailored strategies across model preparation, backend kernel implementations, agile accelerator and SoC design, and inference simulation are incorporated into our framework to refine the development workflow. Our method significantly accelerates the hardware design, simulation, and optimization processes. Experimental results illustrate the impressive speed and effectiveness of our framework in designing edge LLM accelerators and optimizing LLM inference.