上海科技大学知识管理系统

AI chip scales expediently in the large language models (LLMs) era. In contrast, the existing chip design space exploration (DSE) methods, aimed at discovering optimal yet often infeasible or un-produceable Pareto-front designs, are hindered by neglect of design specifications. In this paper, we propose a novel Spec-driven transformed Bayesian optimization framework to find expected optimal RISC-V SoC architecture designs for LLM tasks. The highlights of our framework lie in a tailored transformed Gaussian process (GP) model prioritizing specified target metrics and a customized acquisition function (EHRM) in multi-objective optimization. Extensive experiments on large-scale RISC-V SoC architecture design explorations for LLMs, such as Transformer, BERT, and GPT-1, demonstrate that our method not only can effectively find the design according to QoR values from the spec, but also outperforms 34.59% in ADRS over state-of-the-art approach with only 66.67% runtime overhead. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.