ADMM-Based Training for Spiking Neural Networks

• URL: http://arxiv.org/abs/2505.05527v1
• Date: Thu, 08 May 2025 10:20:33 GMT
• Title: ADMM-Based Training for Spiking Neural Networks
• Authors: Giovanni Perin, Cesare Bidini, Riccardo Mazzieri, Michele Rossi,
• Abstract summary: spiking neural networks (SNNs) have gained momentum due to their high potential in time-series processing combined with minimal energy consumption.<n>They still lack a dedicated and efficient training algorithm.<n>We propose a novel SNN training method based on the alternating direction method of multipliers (ADMM)
• Score: 1.1249583407496218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, spiking neural networks (SNNs) have gained momentum due to their high potential in time-series processing combined with minimal energy consumption. However, they still lack a dedicated and efficient training algorithm. The popular backpropagation with surrogate gradients, adapted from stochastic gradient descent (SGD)-derived algorithms, has several drawbacks when used as an optimizer for SNNs. Specifically, it suffers from low scalability and numerical imprecision. In this paper, we propose a novel SNN training method based on the alternating direction method of multipliers (ADMM). Our ADMM-based training aims to solve the problem of the SNN step function's non-differentiability. We formulate the problem, derive closed-form updates, and empirically show the optimizer's convergence properties, great potential, and possible new research directions to improve the method in a simulated proof-of-concept.

Related papers

• Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm [0.13499500088995461]
  Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm that emulates neuronal activity through discrete spike-based processing.<n>Despite their advantages, training SNNs with traditional backpropagation (BP) remains challenging due to computational inefficiencies and a lack of biological plausibility.<n>This study explores the Forward-Forward (FF) algorithm as an alternative learning framework for SNNs.
  arXiv  Detail & Related papers  (2025-02-19T12:44:26Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval.<n>A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed.<n>The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• MARS: Unleashing the Power of Variance Reduction for Training Large Models [56.47014540413659]
  We propose a unified training framework for deep neural networks.<n>We introduce three instances of MARS that leverage preconditioned gradient optimization.<n>Results indicate that the implementation of MARS consistently outperforms Adam.
  arXiv  Detail & Related papers  (2024-11-15T18:57:39Z)
• Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning [4.178826560825283]
  Spiking neural networks (SNNs) represent a promising approach in machine learning, combining the hierarchical learning capabilities of deep neural networks with the energy efficiency of spike-based computations. Traditional end-to-end training of SNNs is often based on back-propagation, where weight updates are derived from gradients computed through the chain rule. This method encounters challenges due to its limited biological plausibility and inefficiencies on neuromorphic hardware. In this study, we introduce an alternative training approach for SNNs. Instead of using back-propagation, we leverage weight perturbation methods within a forward-mode
  arXiv  Detail & Related papers  (2024-11-11T15:20:54Z)
• Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network [10.760652747217668]
  Spiking neural network (SNN) is studied in multidisciplinary domains to simulate neuro-scientific mechanisms. The lack of discrete theory obstructs the practical application of SNN by limiting its performance and nonlinearity support. We present a new optimization-theoretic perspective of the discrete dynamics of spiking neurons.
  arXiv  Detail & Related papers  (2024-07-01T02:09:20Z)
• Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation [49.44309457870649]
  Layer-wise Feedback feedback (LFP) is a novel training principle for neural network-like predictors.<n>LFP decomposes a reward to individual neurons based on their respective contributions.<n>Our method then implements a greedy reinforcing approach helpful parts of the network and weakening harmful ones.
  arXiv  Detail & Related papers  (2023-08-23T10:48:28Z)
• Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks [51.92362217307946]
  Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems. PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
  arXiv  Detail & Related papers  (2023-03-03T08:17:47Z)
• SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks [56.35403810762512]
  Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. We study spike-based implicit differentiation on the equilibrium state (SPIDE) that extends the recently proposed training method.
  arXiv  Detail & Related papers  (2023-02-01T04:22:59Z)
• 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)
• Exact Gradient Computation for Spiking Neural Networks Through Forward Propagation [39.33537954568678]
  Spiking neural networks (SNN) have emerged as alternatives to traditional neural networks. We propose a novel training algorithm, called emphforward propagation (FP), that computes exact gradients for SNN.
  arXiv  Detail & Related papers  (2022-10-18T20:28:21Z)
• FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning [14.924672048447338]
  We devise a dynamic network architecture for continual learning based on a novel forgetting-free neural block (FFNB) Training FFNB features on new tasks is achieved using a novel procedure that constrains the underlying parameters in the null-space of the previous tasks.
  arXiv  Detail & Related papers  (2021-11-22T17:23:34Z)
• Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [66.2457134675891]
  Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. Most existing methods imitate the backpropagation framework and feedforward architectures for artificial neural networks. We propose a novel training method that does not rely on the exact reverse of the forward computation.
  arXiv  Detail & Related papers  (2021-09-29T07:46:54Z)
• A Novel Neural Network Training Framework with Data Assimilation [2.948167339160823]
  A gradient-free training framework based on data assimilation is proposed to avoid the calculation of gradients. The results show that the proposed training framework performed better than the gradient decent method.
  arXiv  Detail & Related papers  (2020-10-06T11:12:23Z)

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.