Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks

15 Jun 2017Hesham MostafaBruno PedroniSadique SheikGert Cauwenberghs

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs... (read more)

PDF Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper