Harnessing Numerical Flexibility for Deep Learning on FPGAs White Paper - In this paper, we explore FPGA minifloat implementations (floating-point representations with non-standard exponent and mantissa sizes), and show the use of a block-floating point implementation that shares the exponent across many numbers, reducing the logic required to perform floating-point operations. - 2018-11-06
wp-01281-harnessing-numerical-flexibility-for-deep-learning-on-fpgas.pdf
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