Computation-intensive kernels, such as convolutions, matrix multiplication, and Fourier transform, are fundamental to edge-computing AI, signal processing, and cryptographic applications. Interleaved-multithreading (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 article 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.

中文翻译：

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Klessydra-T：设计用于多线程边缘计算核心的矢量协处理器

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程序的基础。交错多线程（IMT）处理器内核对于提高能效和降低边缘计算的硬件成本很有趣，但是它们需要硬件加速方案来运行繁重的计算工作量。遵循矢量方法来加快计算速度，本文探索了在RISC-V内核中实现矢量协处理单元的可能替代方法，展示了目标工作负载中IMT和数据级并行性之间的协同作用。