Abstract: Considering the high-performance and low-power requirements of edge AI,this paper designs a specialized instruction set processor for edge AI based on the RISC-V instruction set architecture,addressing practical issues in digital signal processing for edge devices.This design enhances the execution efficiency of edge AI and reduces its energy consumption with limited hardware overhead,meeting the demands for efficient large language model(LLM) inference computation in edge AI applications.For the characteristics of large language models,custom instructions were extended based on the RISC-V instruction set to perform vector dot product calculations,accelerating the computation of large language models on dedicated vector dot product acceleration hardware.Based on the open-source high-performance RISC-V processor core XiangShan Nanhu architecture,the vector dot product specialized instruction set processor Nanhu-vdot is implemented,which adds vector dot product calculation units and pipeline processing logic on top of the XiangShan Nanhu.The Nanhu-vdot underwent FPGA hardware testing achieves over four times of the speed of scalar methods in vector dot product computation.Using a hardware-software co-design approach for second-generation generative pre-trained Transformer(GPT-2) model inference,the speed improves by approximately 30% compared to pure software implementation with almost no additional consumption of hardware resources and power consumption.

Key words: Instruction set extension, Vector dot product, Software and hardware collaboration, Large language model inference