67 citations · 80 across the 4 of their papers we have counts for
4 papers · 1 filter
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic
Jiang Su, Nicholas J. Fraser, Giulio Gambardella +5
Modern CNN are typically based on floating point linear algebra based implementations. Recently, reduced precision NN have been gaining popularity as they require significantly les…
SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks
Julian Faraone, Nicholas Fraser, Michaela Blott +1
Inference for state-of-the-art deep neural networks is computationally expensive, making them difficult to deploy on constrained hardware environments. An efficient way to reduce t…
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks
Julian Faraone, Nicholas Fraser, Giulio Gambardella +2
A low precision deep neural network training technique for producing sparse, ternary neural networks is presented. The technique incorporates hard- ware implementation costs during…
Scaling Binarized Neural Networks on Reconfigurable Logic
Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella +4
Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suit…