RISC-V R扩展：利用租用流水线提升边缘DNN处理效率
RISC-V R-Extension: Advancing Efficiency with Rented-Pipeline for Edge DNN Processing
AI总结：
本文提出RISC-V R扩展，通过租用流水线级和架构流水线寄存器优化关键操作执行，降低延迟和内存访问频率，并引入自定义指令，从而提升边缘设备上DNN推理的处理效率。
摘要：
边缘设备的普及要求为轻量级任务提供高效的计算架构，尤其是深度神经网络（DNN）推理。传统NPU虽然在此类运算中表现有效，但在集成到轻量级边缘设备时面临功耗、成本和面积方面的挑战。RISC-V架构以其模块化和开源特性著称，提供了一种可行的替代方案。本文介绍了RISC-V R扩展，这是一种提升边缘设备上DNN处理效率的新方法。该扩展具有租用流水线级和架构流水线寄存器（APR），可优化关键操作的执行，从而降低延迟和内存访问频率。此外，该扩展还包括新的自定义指令以支持这些架构改进。通过全面分析，本研究证明了R扩展在边缘设备处理中的提升作用，为更具响应性和智能化的边缘应用奠定了基础。
英文摘要：
The proliferation of edge devices necessitates efficient computational architectures for lightweight tasks, particularly deep neural network (DNN) inference. Traditional NPUs, though effective for such operations, face challenges in power, cost, and area when integrated into lightweight edge devices. The RISC-V architecture, known for its modularity and open-source nature, offers a viable alternative. This paper introduces the RISC-V R-extension, a novel approach to enhancing DNN process efficiency on edge devices. The extension features rented-pipeline stages and architectural pipeline registers (APR), which optimize critical operation execution, thereby reducing latency and memory access frequency. Furthermore, this extension includes new custom instructions to support these architectural improvements. Through comprehensive analysis, this study demonstrates the boost of R-extension in edge device processing, setting the stage for more responsive and intelligent edge applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
