67 citations · 106 across the 2 of their papers we have counts for
3 papers
Efficient Error-Tolerant Quantized Neural Network Accelerators
Giulio Gambardella, Johannes Kappauf, Michaela Blott +4
Neural Networks are currently one of the most widely deployed machine learning algorithms. In particular, Convolutional Neural Networks (CNNs), are gaining popularity and are evalu…
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…
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…