Search Results for author: Nicholas Fraser

Found 5 papers, 1 papers with code

FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks

no code implementations12 Sep 2018 Michaela Blott, Thomas Preusser, Nicholas Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu

Given a neural network description, the tool optimizes for given platforms, design targets and a specific precision.

Hardware Architecture

SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks

1 code implementation CVPR 2018 Julian Faraone, Nicholas Fraser, Michaela Blott, Philip H. W. Leong

An efficient way to reduce this complexity is to quantize the weight parameters and/or activations during training by approximating their distributions with a limited entry codebook.


Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices

no code implementations21 Jun 2018 Thomas B. Preußer, Giulio Gambardella, Nicholas Fraser, Michaela Blott

Neural networks have established as a generic and powerful means to approach challenging problems such as image classification, object detection or decision making.

Decision Making Image Classification +3

Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines

no code implementations21 May 2018 Sean O. Settle, Manasa Bollavaram, Paolo D'Alberto, Elliott Delaye, Oscar Fernandez, Nicholas Fraser, Aaron Ng, Ashish Sirasao, Michael Wu

Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy.


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