1k citations · 1.3k across the 12 of their papers we have counts for
12 papers
On the RTL Implementation of FINN Matrix Vector Compute Unit
Syed Asad Alam, David Gregg, Giulio Gambardella +2
FPGA-based accelerators are becoming more popular for deep neural network due to the ability to scale performance with increasing degree of specialization with dataflow architectur…
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults
Ussama Zahid, Giulio Gambardella, Nicholas J. Fraser +2
Deep neural networks (DNNs) are state-of-the-art algorithms for multiple applications, spanning from image classification to speech recognition. While providing excellent accuracy,…
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…
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
Yifan Yang, Qijing Huang, Bichen Wu +8
Using FPGAs to accelerate ConvNets has attracted significant attention in recent years. However, FPGA accelerator design has not leveraged the latest progress of ConvNets. As a res…
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…
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs
Vladimir Rybalkin, Alessandro Pappalardo, Muhammad Mohsin Ghaffar +3
It is well known that many types of artificial neural networks, including recurrent networks, can achieve a high classification accuracy even with low-precision weights and activat…
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…
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…
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella +4
Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced…