Advancements in AI have increased the demand for specialized AI accelerators, with design for general matrix multiplication (GEMM) module being crucial but time-consuming. While large language models (LLMs) show promise for automating GEMM design, challenges arise from GEMM's vast design space and performance requirements. Existing LLM-based frameworks for RTL code generation often lack flexibility and performance awareness. To overcome the challenges, we propose LLM4GV, a multi-agent LLM-based framework that integrates hardware optimization techniques (HOTs) and performance modeling, improving correctness and performance of the generated code over prior works.

Due to the demand for high energy efficiency in deep neural network (DNN) accelerators, computing-in-memory (CIM) is becoming increasingly popular in recent years. However, current CIM designs suffer from high latency and insufficient flexibility. To address the issues, this brief proposes a Booth-multiplication-based CIM macro (BCIM) with modified Booth encoding and partial product (PP) generation method specially designed for CIM architecture. In addition, a methodology is presented for designing precision-reconfigurable digital CIM macros. We also optimize the precision-reconfigurable shift adder in the macro based on the cutting down carry connection method. The design attains a performance of 2048 GOPS and a peak energy efficiency of 79.15 TOPS/W in the signed INT4 mode at a frequency of 500 MHz.