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01/27/2022
On the RTL Implementation of FINN Matrix Vector Compute Unit
FPGA-based accelerators are becoming more popular for deep neural networ...
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11/11/2020
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults
Deep neural networks (DNNs) are state-of-the-art algorithms for multiple...
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12/16/2019
Efficient Error-Tolerant Quantized Neural Network Accelerators
Neural Networks are currently one of the most widely deployed machine le...
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11/21/2018
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
Using FPGAs to accelerate ConvNets has attracted significant attention i...
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09/12/2018
FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks
Convolutional Neural Networks have rapidly become the most successful ma...
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07/11/2018
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs
It is well known that many types of artificial neural networks, includin...
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06/21/2018
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices
Neural networks have established as a generic and powerful means to appr...
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09/19/2017
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks
A low precision deep neural network training technique for producing spa...
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01/12/2017
Scaling Binarized Neural Networks on Reconfigurable Logic
Binarized neural networks (BNNs) are gaining interest in the deep learni...
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12/01/2016
