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Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to high-performance implementations.
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P. Judd et al., “Stripes: Bit-serial deep neural network computing,” in MICRO
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Cited alongside, same era.
E. Park et al., “Weighted-entropy-based quantization for deep neural networks,” in CVPR
2017
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2017
Cited alongside, same era.
Y. Umuroglu et al., “FINN: A framework for fast, scalable binarized neural network inference,” in FPGA
2017
Cited alongside, same era.
T. B. Preußer, “Generic and universal parallel matrix summation with a flexible compression goal for Xilinx FPGAs,” in FPL
2017
Later among the works it cites.
D. J. Moss et al., “A customizable matrix multiplication framework for the Intel HARPv2 Xeon+ FPGA platform: A deep learning case study,” in FPGA
2018
Closest in time.
F. Pedersoli et al., “Espresso: Efficient forward propagation for BCNNs,” in ICLR
2018
Closest in time.
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