LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization

• URL: http://arxiv.org/abs/2401.14652v2
• Date: Mon, 13 May 2024 05:30:29 GMT
• Title: LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
• Authors: Qianhui Liu, Jiaqi Yan, Malu Zhang, Gang Pan, Haizhou Li,
• Abstract summary: Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient. We propose a new approach named LitE-SNN that incorporates both spatial and temporal compression into the automated network design process.
• Score: 48.41286573672824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. However, the pursuit of accuracy in current studies leads to large, long-timestep SNNs, conflicting with the resource constraints of these devices. In order to design lightweight and efficient SNNs, we propose a new approach named LitE-SNN that incorporates both spatial and temporal compression into the automated network design process. Spatially, we present a novel Compressive Convolution block (CompConv) to expand the search space to support pruning and mixed-precision quantization. Temporally, we are the first to propose a compressive timestep search to identify the optimal number of timesteps under specific computation cost constraints. Finally, we formulate a joint optimization to simultaneously learn the architecture parameters and spatial-temporal compression strategies to achieve high performance while minimizing memory and computation costs. Experimental results on CIFAR-10, CIFAR-100, and Google Speech Command datasets demonstrate our proposed LitE-SNNs can achieve competitive or even higher accuracy with remarkably smaller model sizes and fewer computation costs.

Related papers

• Scalable Mechanistic Neural Networks [52.28945097811129]
  We propose an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources.
  arXiv  Detail & Related papers  (2024-10-08T14:27:28Z)
• Q-SNNs: Quantized Spiking Neural Networks [12.719590949933105]
  Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an event-driven manner. We introduce a lightweight and hardware-friendly Quantized SNN that applies quantization to both synaptic weights and membrane potentials. We present a new Weight-Spike Dual Regulation (WS-DR) method inspired by information entropy theory.
  arXiv  Detail & Related papers  (2024-06-19T16:23:26Z)
• Parallel Hyperparameter Optimization Of Spiking Neural Network [0.5371337604556311]
  Spiking Neural Networks (SNNs) are based on a more biologically inspired approach than usual artificial neural networks. We tackle the signal loss issue of SNNs to what we call silent networks. By defining an early stopping criterion, we were able to instantiate larger and more flexible search spaces.
  arXiv  Detail & Related papers  (2024-03-01T11:11:59Z)
• Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization [1.0235078178220354]
  We propose an automated framework to compress Deep Neural Networks (DNNs) in a hardware-aware manner by jointly employing pruning and quantization. Our framework achieves $39%$ average energy reduction for datasets $1.7%$ average accuracy loss and outperforms significantly the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-12-23T18:50:13Z)
• Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks [11.19282454437627]
  Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient networks. It is difficult to deploy these networks on resource-limited edge devices directly. We propose an improved end-to-end Minimax optimization method for this sparse learning problem.
  arXiv  Detail & Related papers  (2023-08-09T02:50:15Z)
• Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML [4.2019872499238256]
  We propose a novel strategy for deploying Deep Neural Networks on microcontrollers (TinyML) based on Multi-Objective Bayesian optimization (MOBOpt) Our methodology aims at efficiently finding tradeoffs between a DNN's predictive accuracy, memory consumption on a given target system, and computational complexity.
  arXiv  Detail & Related papers  (2023-05-23T14:31:52Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression [56.83861738731913]
  Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices. Previous unstructured or structured weight pruning methods can hardly truly accelerate inference. We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
  arXiv  Detail & Related papers  (2021-06-15T17:22:59Z)
• MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search [94.80212602202518]
  We propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS) We employ a one-shot architecture search approach in order to obtain a reduced search cost. We achieve state-of-the-art results in terms of accuracy-speed trade-off.
  arXiv  Detail & Related papers  (2020-09-29T11:56:01Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)

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.