Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses

• URL: http://arxiv.org/abs/2108.01804v2
• Date: Sat, 25 Sep 2021 09:15:54 GMT
• Title: Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
• Authors: Yigit Demirag, Charlotte Frenkel, Melika Payvand, Giacomo Indiveri
• Abstract summary: Training spiking neural networks (RNNs) on dedicated neuromorphic hardware is still an open challenge. We present a simulation framework of differential-architecture arrays based on an accurate and comprehensive Phase-Change Memory (PCM) device model. We train a spiking RNN whose weights are emulated in the presented simulation framework, using a recently proposed e-prop learning rule.
• Score: 1.9809266426888898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network dynamics, even when the weight resolution is limited. These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing to resolve the von-Neumann bottleneck problem, at the expense of a substantial increase in variability in both the computation and the working memory of the spiking RNNs. To address these challenges and enable online learning in memristive neuromorphic RNNs, we present a simulation framework of differential-architecture crossbar arrays based on an accurate and comprehensive Phase-Change Memory (PCM) device model. We train a spiking RNN whose weights are emulated in the presented simulation framework, using a recently proposed e-prop learning rule. Although e-prop locally approximates the ideal synaptic updates, it is difficult to implement the updates on the memristive substrate due to substantial PCM non-idealities. We compare several widely adapted weight update schemes that primarily aim to cope with these device non-idealities and demonstrate that accumulating gradients can enable online and efficient training of spiking RNN on memristive substrates.

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)
• Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation [6.233189707488025]
  In this article, we analyze the dynamical, computational, and learning properties of adaptive LIF neurons and networks thereof. We show that the superiority of networks of adaptive LIF neurons extends to the prediction and generation of complex time series.
  arXiv  Detail & Related papers  (2024-08-14T12:49:58Z)
• Synaptic metaplasticity with multi-level memristive devices [1.5598974049838272]
  We propose a memristor-based hardware solution for implementing metaplasticity during both inference and training. We show that a two-layer perceptron achieves 97% and 86% accuracy on consecutive training of MNIST and Fashion-MNIST. Our architecture is compatible with the memristor limited endurance and has a 15x reduction in memory.
  arXiv  Detail & Related papers  (2023-06-21T09:40:25Z)
• 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)
• 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)
• On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks [1.7958576850695402]
  We propose a local, gradient-based, error-triggered learning algorithm with online ternary weight updates. The proposed algorithm enables online training of multi-layer SNNs with memristive neuromorphic hardware.
  arXiv  Detail & Related papers  (2020-11-21T19:44:19Z)
• Skip-Connected Self-Recurrent Spiking Neural Networks with Joint Intrinsic Parameter and Synaptic Weight Training [14.992756670960008]
  We propose a new type of RSNN called Skip-Connected Self-Recurrent SNNs (ScSr-SNNs) ScSr-SNNs can boost performance by up to 2.55% compared with other types of RSNNs trained by state-of-the-art BP methods.
  arXiv  Detail & Related papers  (2020-10-23T22:27:13Z)
• A Fully Tensorized Recurrent Neural Network [48.50376453324581]
  We introduce a "fully tensorized" RNN architecture which jointly encodes the separate weight matrices within each recurrent cell. This approach reduces model size by several orders of magnitude, while still maintaining similar or better performance compared to standard RNNs.
  arXiv  Detail & Related papers  (2020-10-08T18:24:12Z)
• 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)
• Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks [0.9449650062296823]
  Neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. We study Spiking Neural Networks in two neuromorphic architecture implementations with the goal of decreasing their size. We propose a multiobjective fitness function to optimize the size and resiliency of the SNN.
  arXiv  Detail & Related papers  (2020-02-04T16:58:25Z)

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.