Mix-GEMM: Extending RISC-V CPUs for Energy-Efficient Mixed-Precision DNN Inference Using Binary Segmentation
IEEE TRANSACTIONS ON COMPUTERS（2025）
Key words
Program processors,Computer architecture,Energy efficiency,Quantization (signal),Artificial neural networks,Computers,Computational modeling,Pipelines,Software,Single instruction multiple data,Deep neural networks,RISC-V extensions,energy efficiency,binary segmentation,neural accelerators
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