Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA

• URL: http://arxiv.org/abs/2305.19468v1
• Date: Wed, 31 May 2023 00:34:15 GMT
• Title: Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA
• Authors: Ali Mehrabi, Yeshwanth Bethi, Andr\'e van Schaik, Andrew Wabnitz, Saeed Afshar
• Abstract summary: ODESA is the first network to have end-to-end multi-layer online local supervised training without using gradients. This research shows that the network architecture and the online training of weights and thresholds can be implemented efficiently on a large scale in hardware.
• Score: 0.31498833540989407
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first network to have end-to-end multi-layer online local supervised training without using gradients and has the combined adaptation of weights and thresholds in an efficient hierarchical structure. This research shows that the network architecture and the online training of weights and thresholds can be implemented efficiently on a large scale in hardware. The implementation consists of a multi-layer Spiking Neural Network (SNN) and individual training modules for each layer that enable online self-learning without using back-propagation. By using simple local adaptive selection thresholds, a Winner-Takes-All (WTA) constraint on each layer, and a modified weight update rule that is more amenable to hardware, the trainer module allocates neuronal resources optimally at each layer without having to pass high-precision error measurements across layers. All elements in the system, including the training module, interact using event-based binary spikes. The hardware-optimized implementation is shown to preserve the performance of the original algorithm across multiple spatial-temporal classification problems with significantly reduced hardware requirements.

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)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• 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)
• Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration [71.80326738527734]
  We propose a general, fine-grained structured pruning scheme and corresponding compiler optimizations. We show that our pruning scheme mapping methods, together with the general fine-grained structured pruning scheme, outperform the state-of-the-art DNN optimization framework.
  arXiv  Detail & Related papers  (2021-11-22T23:53:14Z)
• An optimised deep spiking neural network architecture without gradients [7.183775638408429]
  We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules. The architecture represents a highly abstracted model of existing Spiking Neural Network (SNN) architectures.
  arXiv  Detail & Related papers  (2021-09-27T05:59:12Z)
• Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design [1.6066483376871004]
  This work proposes a novel hardware-software co-design method that enables robust and noise-resilient Multi-task Learning in D$2$NNs. Our experimental results demonstrate significant improvements in versatility and hardware efficiency, and also demonstrate the robustness of proposed multi-task D$2$NN architecture.
  arXiv  Detail & Related papers  (2020-12-16T12:29:54Z)
• 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)
• Smoother Network Tuning and Interpolation for Continuous-level Image Processing [7.730087303035803]
  Filter Transition Network (FTN) is a structurally smoother module for continuous-level learning. FTN generalizes well across various tasks and networks and cause fewer undesirable side effects. For stable learning of FTN, we additionally propose a method to non-linear neural network layers with identity mappings.
  arXiv  Detail & Related papers  (2020-10-05T18:29:52Z)
• Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks [100.14670789581811]
  We train a graph convolutional network to fit the performance of sampled sub-networks. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates.
  arXiv  Detail & Related papers  (2020-04-17T19:12:39Z)
• Regularized Adaptation for Stable and Efficient Continuous-Level Learning on Image Processing Networks [7.730087303035803]
  We propose a novel continuous-level learning framework using a Filter Transition Network (FTN) FTN is a non-linear module that easily adapt to new levels, and is regularized to prevent undesirable side-effects. Extensive results for various image processing indicate that the performance of FTN is stable in terms of adaptation and adaptation.
  arXiv  Detail & Related papers  (2020-03-11T07:46:57Z)
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment [84.57874289554839]
  Training deep neural networks on large-scale datasets requires significant hardware resources. Backpropagation, the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. We propose a neuro-biologically-plausible alternative to backprop that can be used to train deep networks.
  arXiv  Detail & Related papers  (2020-02-10T16:20:02Z)

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.