Mar 25, 2021 · This technique enables training on an inference-only hardware without the need to use backpropagation and with minimal resource overhead. We ...
Mar 25, 2021 · V. CONCLUSION In this work, we propose an incremental training technique using ES that enables training DNNs on an inference only hardware ...
This paper introduces a method using evolutionary strategy (ES) that can partially retrain the network enabling it to adapt to changes and recover after an ...
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In this paper, we summarize techniques that make training on embedded devices possible. We first describe the need and requirements for such algorithms.
Enabling Incremental Training with Forward Pass for Edge Devices. arXiv.org, 2021-03. 2021. This work is published under http://arxiv.org/licenses ...
Oct 22, 2024 · Efficient training algorithms would enable federated learning on embedded devices, in which the data remains where it was collected, or ...
Apr 9, 2022 · ... forward pass operations via pooling on the convolution filters. Training ... MoIL: ENABLING EFFICIENT INCREMENTAL TRAINING ON EDGE DEVICES
This is because, the time-wise activation tensors are saved on the GPU memory during the forward pass computations since they are required during the backward.
Aug 20, 2024 · Analog in-memory computing is a promising future technology for efficiently accelerating deep learning networks. While using in-memory ...
Nov 22, 2023 · Authors: The target for our technique are edge devices with limited computational resources in scenarios such as human activity recognition.