DNNZip implements a lossy compression whose compression ratio is tuned based on the maximum tolerated error on the model parameters provided by the user. DNNZip is assessed on several convolutional NNs and the trade-off inference energy saving vs.
In this paper, we present DNNZip, a technique aimed at compressing the model parameters of a DNN, thus resulting in significant energy and performance ...
Abstract—In Deep Neural Network (DNN) accelerators, the on- chip traffic and memory traffic accounts for a relevant fraction.
DNNZip: Selective Layers Compression Technique in Deep Neural Network Accelerators ... techniques employed to reduce the redundancy of deep neural networks ...
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H. Lahdhiri et al., "DNNZip: Selective Layers Compression Technique in Deep Neural Network Accelerators," 2020 23rd Euromicro Conference on Digital System ...
May 8, 2023 · The first technique takes into consideration the regularly repeat property of the DNN weights to compress them. The second technique saves the ...
Analyzing the Impact of DNN Hardware Accelerators-Oriented Compression Techniques on General-Purpose Low-End Boards.
May 8, 2023 · This paper proposes a framework for distributed, in-storage training of neural networks on clusters of computational storage devices. Such ...
Feb 1, 2023 · Our study is intended to provide a first and preliminary guidance to choose the most suitable compression technique when there is the need to ...
Missing: DNNZip: Accelerators.
Eyeriss v2, a DNN accelerator architecture designed for running compact and sparse DNNs, is presented, which introduces a highly flexible on-chip network.