IEEE Micro · 2021 · 38 citations · 8 references
ComputingComputer EngineeringComputer ScienceComputer ArchitectureEdge ComputingParallel ComputingData ParallelismEdge ArchitectureEdge Artificial IntelligenceHardware AccelerationMulticore Processor DesignParallel ArchitecturesCryptographic ApplicationsEdge Computing ArchitecturesEdge Computing HardwareMatrix MultiplicationRisc ArchitecturesRisc-v Core DesignVector ProcessingVector Processors
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.
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