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07/25/2023
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation
The unprecedented accuracy of convolutional neural networks (CNNs) acros...
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06/30/2023
High-throughput Simulation of Federated Learning via Resource-Aware Client Placement
Federated Learning (FL) is the privacy-preserving machine learning parad...
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05/25/2023
Are We There Yet? Product Quantization and its Hardware Acceleration
Conventional multiply-accumulate (MAC) operations have long dominated co...
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09/30/2022
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio
Self-supervised learning (SSL) has proven vital in speech and audio-rela...
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08/04/2022
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
When the available hardware cannot meet the memory and compute requireme...
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07/03/2022
Protea: Client Profiling within Federated Systems using Flower
Federated Learning (FL) has emerged as a prospective solution that facil...
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06/22/2022
FedorAS: Federated Architecture Search under system heterogeneity
Federated learning (FL) has recently gained considerable attention due t...
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04/06/2022
Federated Self-supervised Speech Representations: Are We There Yet?
The ubiquity of microphone-enabled devices has lead to large amounts of ...
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04/29/2021
End-to-End Speech Recognition from Federated Acoustic Models
Training Automatic Speech Recognition (ASR) models under federated learn...
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04/07/2021
On-device Federated Learning with Flower
Federated Learning (FL) allows edge devices to collaboratively learn a s...
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03/09/2021
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation
Single computation engines have become a popular design choice for FPGA-...
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08/11/2020
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Graph neural networks (GNNs) have demonstrated strong performance on a w...
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02/25/2020
