Deep neural networks (DNNs) face significant challenges when deployed on resource-constrained extreme edge devices due to their computational and data-intensive nature. While standalone accelerators tailored for specific application scenarios suffer from inflexible control and limited programmability, generic hardware acceleration platforms coupled with RISC-V CPUs can enable high reusability and flexibility, yet typically at the expense of system level efficiency and low utilization. To fill this gap, we propose OpenGeMM, an open-source acceleration platform, jointly demonstrating high efficiency and utilization, as well as ease of configurability and programmability. OpenGeMM encompasses a parameterized Chisel-coded GeMM accelerator, a lightweight RISC-V processor, and a tightly coupled multi-banked scratchpad memory. The GeMM core utilization and system efficiency are boosted through three mechanisms: configuration pre-loading, input pre-fetching with output buffering, and programmable strided memory access. Experimental results show that OpenGeMM can consistently achieve hardware utilization ranging from 81.89% to 99.34% across diverse CNN and Transformer workloads. Compared to the SotA open-source Gemmini accelerator, OpenGeMM demonstrates a 3.58x to 16.40x speedup on normalized throughput across a wide variety ofGeMM workloads, while achieving 4.68 TOPS/W system efficiency.

本研究针对深度神经网络在资源受限的边缘设备上的部署挑战，提出了一种新的开放源代码加速平台OpenGeMM，该平台结合了高效利用和易配置的优势。通过参数化的Chisel编码GeMM加速器、轻量级RISC-V处理器以及紧耦合的多银行临时存储器，实验结果表明OpenGeMM在多种CNN和Transformer工作负载下实现了81.89%至99.34%的硬件利用率，并相比现有的Gemmini加速器在吞吐量上实现了3.58倍至16.40倍的速度提升，且系统效率可达4.68 TOPS/W。

OpenGeMM：一种具有轻量级RISC-V控制和紧耦合内存的高利用率GeMM加速器生成器