Harnessing Numerical Flexibility for Deep Learning on FPGAs White Paper

Description

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.