1k citations · 1.2k across the 11 of their papers we have counts for
6 papers · 1 filter
FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks
Michaela Blott, Thomas Preusser, Nicholas Fraser +3
Convolutional Neural Networks have rapidly become the most successful machine learning algorithm, enabling ubiquitous machine vision and intelligent decisions on even embedded comp…
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…
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic
Michaela Blott, Thomas B. Preusser, Nicholas Fraser +4
Convolutional Neural Networks have dramatically improved in recent years, surpassing human accuracy on certain problems and performance exceeding that of traditional computer visio…
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices
Thomas B. Preußer, Giulio Gambardella, Nicholas Fraser +1
Neural networks have established as a generic and powerful means to approach challenging problems such as image classification, object detection or decision making. Their successfu…
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines
Sean O. Settle, Manasa Bollavaram, Paolo D'Alberto +6
Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy. These computational models have seemingly…