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06/16/2023
Sparq: A Custom RISC-V Vector Processor for Efficient Sub-Byte Quantized Inference
Convolutional Neural Networks (CNNs) are used in a wide range of applica...
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02/12/2023
Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference
In this paper, we present Quark, an integer RISC-V vector processor spec...
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11/18/2022
Sharpness-Aware Training for Accurate Inference on Noisy DNN Accelerators
Energy-efficient deep neural network (DNN) accelerators are prone to non...
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08/10/2022
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming
Hybrid beamforming is a promising technology to improve the energy effic...
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05/03/2022
MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators
Memristors enable the computation of matrix-vector multiplications (MVM)...
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09/03/2021
Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering
This paper presents a quantized Kalman filter implemented using unreliab...
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08/26/2021
Energy Optimization of Faulty Quantized Min-Sum LDPC Decoders
The objective of this paper is to minimize the energy consumption of a q...
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06/30/2021
Limited-Fronthaul Cell-Free Hybrid Beamforming with Distributed Deep Neural Network
Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach...
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06/30/2020
Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming
Hybrid beamforming is a promising technique to reduce the complexity and...
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05/12/2020
Noisy Density Evolution With Asymmetric Deviation Models
This paper considers low-density parity-check (LDPC) decoders affected b...
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03/12/2020
RSSI-Based Hybrid Beamforming Design with Deep Learning
Hybrid beamforming is a promising technology for 5G millimetre-wave comm...
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12/23/2019
Layerwise Noise Maximisation to Train Low-Energy Deep Neural Networks
Deep neural networks (DNNs) depend on the storage of a large number of p...
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11/23/2019
Training Modern Deep Neural Networks for Memory-Fault Robustness
Because deep neural networks (DNNs) rely on a large number of parameters...
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04/18/2017
A Study of Deep Learning Robustness Against Computation Failures
For many types of integrated circuits, accepting larger failure rates in...
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10/18/2016
A 9.52 dB NCG FEC scheme and 164 bits/cycle low-complexity product decoder architecture
Powerful Forward Error Correction (FEC) schemes are used in optical comm...
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09/29/2015
