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.
Klessydra-T: Designing vector coprocessors for multithreaded edge-computing cores / Cheikh, A.; Sordillo, S.; Mastrandrea, A.; Menichelli, F.; Scotti, G.; Olivieri, M.. - In: IEEE MICRO. - ISSN 0272-1732. - 41:2(2021), pp. 64-71. [10.1109/MM.2021.3050962]
Klessydra-T: Designing vector coprocessors for multithreaded edge-computing cores
Cheikh A.;Sordillo S.;Mastrandrea A.;Menichelli F.;Scotti G.;Olivieri M.
2021
Abstract
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.File | Dimensione | Formato | |
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