most citedAddNet: Deep Neural Networks Using FPGA-Optimized Multipliers

67 citations · 67 across the 1 of their papers we have counts for

collaborators
Showing eess.SPShow all

2 papers · 1 filter

eess.SP2019★ 67 cited

AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers

Julian Faraone, Martin Kumm, Martin Hardieck +4

Low-precision arithmetic operations to accelerate deep-learning applications on field-programmable gate arrays (FPGAs) have been studied extensively, because they offer the potenti…

eess.SP2019

Unrolling Ternary Neural Networks

Stephen Tridgell, Martin Kumm, Martin Hardieck +4

The computational complexity of neural networks for large scale or real-time applications necessitates hardware acceleration. Most approaches assume that the network architecture a…