Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning
- URL: http://arxiv.org/abs/2411.07057v1
- Date: Mon, 11 Nov 2024 15:20:54 GMT
- Title: Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning
- Authors: Ruyin Wan, Qian Zhang, George Em Karniadakis,
- Abstract summary: 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
- Score: 4.178826560825283
- License:
- Abstract: 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. However, 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 gradient framework. Specifically, we perturb the weight matrix with a small noise term and estimate gradients by observing the changes in the network output. Experimental results on regression tasks, including solving various PDEs, show that our approach achieves competitive accuracy, suggesting its suitability for neuromorphic systems and potential hardware compatibility.
Related papers
- 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.
A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed.
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) - Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
We propose to represent neural networks as computational graphs of parameters.
Our approach enables a single model to encode neural computational graphs with diverse architectures.
We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
arXiv Detail & Related papers (2024-03-18T18:01:01Z) - Learning fixed points of recurrent neural networks by reparameterizing
the network model [0.0]
In computational neuroscience, fixed points of recurrent neural networks are commonly used to model neural responses to static or slowly changing stimuli.
A natural approach is to use gradient descent on the Euclidean space of synaptic weights.
We show that this approach can lead to poor learning performance due to singularities that arise in the loss surface.
arXiv Detail & Related papers (2023-07-13T13:09:11Z) - 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) - Variational Tensor Neural Networks for Deep Learning [0.0]
We propose an integration of tensor networks (TN) into deep neural networks (NNs)
This in turn, results in a scalable tensor neural network (TNN) architecture capable of efficient training over a large parameter space.
We validate the accuracy and efficiency of our method by designing TNN models and providing benchmark results for linear and non-linear regressions, data classification and image recognition on MNIST handwritten digits.
arXiv Detail & Related papers (2022-11-26T20:24:36Z) - 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) - 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) - Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism [1.6114012813668932]
Physics-Informed Neural Networks (PINNs) have emerged as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs)
We propose a fundamentally new way to train PINNs adaptively, where the adaptation weights are fully trainable and applied to each training point individually.
In numerical experiments with several linear and nonlinear benchmark problems, the SA-PINN outperformed other state-of-the-art PINN algorithm in L2 error.
arXiv Detail & Related papers (2020-09-07T04:07:52Z) - 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.