LLM-AID: Leveraging Large Language Models for Rapid Domain-Specific Accelerator Development

Abstract

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The challenges posed by the Dark Silicon era, combined with the escalating computational demands of emerging applications, such as Deep Learning (DL), have strained the capabilities of traditional CPUs and GPUs, necessitating the development of Domain-Specific Accelerators (DSAs). Despite offering substantial enhancements in Power, Performance, and Area (PPA), DSAs encounter significant challenges, including the rapid evolution of applications that necessitate the frequent development of new architectures. This, coupled with the expertise-intensive nature of the design process, often leads to reduced flexibility and extended development cycles, ultimately hindering the broader adoption and efficient deployment of DSAs. To address these challenges, this paper introduces LLM-AID, an agile framework that streamlines the DSA design flow by transforming high-level abstract specifications into Hardware Description Language (HDL) code and facilitating backend Computer-Aided Design (CAD) tool operations. By synergistically combining Large Language Models (LLMs), High-Level Synthesis (HLS) tools, design exploration techniques, and symbolic AI, LLM-AID dramatically accelerates design iterations, optimizes hardware performance, and significantly reduces time-to-market. This innovative approach democratizes DSA development, empowering designers to achieve unprecedented productivity while delivering high-quality DSA solutions.