67 citations · 107 across the 3 of their papers we have counts for
4 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…
Model-based Hardware Design for FPGAs using Folding Transformations based on Subcircuits
Konrad Möller, Martin Kumm, Charles-Frederic Müller +1
We present a tool flow and results for a model-based hardware design for FPGAs from Simulink descriptions which nicely integrates into existing environments. While current commerci…