Klessydra-T：为多线程边缘计算内核设计向量协处理器
Klessydra-T: Designing Vector Coprocessors for Multi-Threaded Edge-Computing Cores
摘要：
卷积、矩阵乘法和傅立叶变换等计算密集型内核是边缘计算AI、信号处理和密码学应用的基础。交错多线程（IMT）处理器内核在追求边缘计算的能效和低硬件成本方面很有意思，但它们需要硬件加速方案来运行繁重的计算工作负载。遵循加速计算的矢量方法，本研究探索了在RISC-V内核中实现矢量协处理单元的可能替代方案，显示了目标工作负载中IMT和数据级并行性之间的协同作用。
英文摘要：
Computation intensive kernels, such as convolutions, matrix multiplication and Fourier transform, are fundamental to edge-computing AI, signal processing and cryptographic applications. Interleaved-Multi-Threading (IMT) processor cores are interesting to pursue energy efficiency and low hardware cost for edge-computing, yet they need hardware acceleration schemes to run heavy computational workloads. Following a vector approach to accelerate computations, this study explores possible alternatives to implement vector coprocessing units in RISC-V cores, showing the synergy between IMT and data-level parallelism in the target workloads.
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。
