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12/09/2022
Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays
In this work, we demonstrate the offline FPGA realization of both recurr...
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09/28/2022
LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors
This work proposes a novel reconfigurable architecture for low latency G...
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06/24/2022
Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems
For the first time, recurrent and feedforward neural network-based equal...
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02/04/2022
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators
Dilated and transposed convolutions are widely used in modern convolutio...
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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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11/14/2020
Memory-Efficient Dataflow Inference for Deep CNNs on FPGA
Custom dataflow Convolutional Neural Network (CNN) inference accelerator...
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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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04/06/2020
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications
Deployment of deep neural networks for applications that require very hi...
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03/24/2020
Evolutionary Bin Packing for Memory-Efficient Dataflow Inference Acceleration on FPGA
Convolutional neural network (CNN) dataflow inference accelerators imple...
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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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09/11/2019
QuTiBench: Benchmarking Neural Networks on Heterogeneous Hardware
Neural Networks have become one of the most successful universal machine...
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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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07/01/2018
SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks
Inference for state-of-the-art deep neural networks is computationally e...
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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
