Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data

• URL: http://arxiv.org/abs/2107.08815v1
• Date: Fri, 16 Jul 2021 07:17:26 GMT
• Title: Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data
• Authors: Jiandong Mu, Mengdi Wang, Feiwen Zhu, Jun Yang, Wei Lin, Wei Zhang
• Abstract summary: Reinforcement learning (RL)-based auto-pruning has been proposed to automate the pruning process to avoid expensive hand-crafted work. However, the RL-based pruner involves a time-consuming training process and the high expense of each sample further exacerbates this problem. We propose an efficient auto-pruning framework which solves this problem by taking advantage of the historical data from the previous auto-pruning process.
• Score: 35.36703623383735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for applications in power-constrained scenarios such as embedded systems. Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work. However, the RL-based pruner involves a time-consuming training process and the high expense of each sample further exacerbates this problem. These impediments have greatly restricted the real-world application of RL-based auto-pruning. Thus, in this paper, we propose an efficient auto-pruning framework which solves this problem by taking advantage of the historical data from the previous auto-pruning process. In our framework, we first boost the convergence of the RL-pruner by transfer learning. Then, an augmented transfer learning scheme is proposed to further speed up the training process by improving the transferability. Finally, an assistant learning process is proposed to improve the sample efficiency of the RL agent. The experiments have shown that our framework can accelerate the auto-pruning process by 1.5-2.5 times for ResNet20, and 1.81-2.375 times for other neural networks like ResNet56, ResNet18, and MobileNet v1.

Related papers

• Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
  Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
  arXiv  Detail & Related papers  (2024-03-21T02:33:37Z)
• Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving [63.155562267383864]
  Deep reinforcement learning (DRL) has shown remarkable success in complex autonomous driving scenarios. DRL models inevitably bring high memory consumption and computation, which hinders their wide deployment in resource-limited autonomous driving devices. We introduce a novel dynamic structured pruning approach that gradually removes a DRL model's unimportant neurons during the training stage.
  arXiv  Detail & Related papers  (2024-02-07T09:00:30Z)
• Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models [17.34908967455907]
  machine unlearning'' proposes the selective removal of unwanted data without the need for retraining from scratch. Fast-NTK is a novel NTK-based unlearning algorithm that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2023-12-22T18:55:45Z)
• Digital Twin Assisted Deep Reinforcement Learning for Online Admission Control in Sliced Network [19.152875040151976]
  We propose a digital twin (DT) accelerated DRL solution to address this issue. A neural network-based DT is established with a customized output layer for queuing systems, trained through supervised learning, and then employed to assist the training phase of the DRL model. Extensive simulations show that the DT-accelerated DRL improves resource utilization by over 40% compared to the directly trained state-of-the-art dueling deep Q-learning model.
  arXiv  Detail & Related papers  (2023-10-07T09:09:19Z)
• Solving Large-scale Spatial Problems with Convolutional Neural Networks [88.31876586547848]
  We employ transfer learning to improve training efficiency for large-scale spatial problems. We propose that a convolutional neural network (CNN) can be trained on small windows of signals, but evaluated on arbitrarily large signals with little to no performance degradation.
  arXiv  Detail & Related papers  (2023-06-14T01:24:42Z)
• Efficient Real Time Recurrent Learning through combined activity and parameter sparsity [0.5076419064097732]
  Backpropagation through time (BPTT) is the standard algorithm for training recurrent neural networks (RNNs) BPTT is unsuited for online learning and presents a challenge for implementation on low-resource real-time systems. We show that recurrent networks exhibiting high activity sparsity can reduce the computational cost of Real-Time Recurrent Learning (RTRL)
  arXiv  Detail & Related papers  (2023-03-10T01:09:04Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training [4.124948554183487]
  We propose a supervised training procedure for RSNNs, where a second network is introduced only during the training. The proposed training procedure consists of generating targets for both recurrent and readout layers. We demonstrate the improved performance and noise robustness of the proposed full-FORCE training procedure to model 8 dynamical systems.
  arXiv  Detail & Related papers  (2022-05-26T19:01:19Z)
• Online Convolutional Re-parameterization [51.97831675242173]
  We present online convolutional re- parameterization (OREPA), a two-stage pipeline, aiming to reduce the huge training overhead by squeezing the complex training-time block into a single convolution. Compared with the state-of-the-art re-param models, OREPA is able to save the training-time memory cost by about 70% and accelerate the training speed by around 2x. We also conduct experiments on object detection and semantic segmentation and show consistent improvements on the downstream tasks.
  arXiv  Detail & Related papers  (2022-04-02T09:50:19Z)
• Single-Shot Pruning for Offline Reinforcement Learning [47.886329599997474]
  Deep Reinforcement Learning (RL) is a powerful framework for solving complex real-world problems. One way to tackle this problem is to prune neural networks leaving only the necessary parameters. We close the gap between RL and single-shot pruning techniques and present a general pruning approach to the Offline RL.
  arXiv  Detail & Related papers  (2021-12-31T18:10:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.