Streamlined Deployment for Quantized Neural Networks

1 code implementation • 12 Sep 2017 • Yaman Umuroglu, Magnus Jahre

Quantized Neural Networks (QNNs) have emerged as a potential solution to this problem, promising to offer most of the DNN accuracy benefits with much lower computational cost.

Scaling Binarized Neural Networks on Reconfigurable Logic

no code implementations • 12 Jan 2017 • Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers

Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost.

General Classification

FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

4 code implementations • 1 Dec 2016 • Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers

Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values.

General Classification