RISC-V R-Extension: Advancing Efficiency with Rented-Pipeline for Edge DNN Processing
<|reference_start|>RISC-V R-Extension: Advancing Efficiency with Rented-Pipeline for Edge DNN Processing: 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.<|reference_end|>
@article{kim2024risc-v,
    title={RISC-V R-Extension: Advancing Efficiency with Rented-Pipeline for Edge
  DNN Processing},
    author={Won Hyeok Kim, Hyeong Jin Kim and Tae Hee Han},
    journal={arXiv preprint arXiv:2407.02622},
    year={2024},
    doi={10.1109/ICAIIC60209.2024.10463391},
    archivePrefix={arXiv},
    eprint={2407.02622},
    primaryClass={cs.AR cs.AI}
}