On-Chip Error-triggered Learning of Multi-layer Memristive Spiking
Neural Networks
- URL: http://arxiv.org/abs/2011.10852v1
- Date: Sat, 21 Nov 2020 19:44:19 GMT
- Title: On-Chip Error-triggered Learning of Multi-layer Memristive Spiking
Neural Networks
- Authors: Melika Payvand, Mohammed E. Fouda, Fadi Kurdahi, Ahmed M. Eltawil,
Emre O. Neftci
- Abstract summary: 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.
- Score: 1.7958576850695402
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent breakthroughs in neuromorphic computing show that local forms of
gradient descent learning are compatible with Spiking Neural Networks (SNNs)
and synaptic plasticity. Although SNNs can be scalably implemented using
neuromorphic VLSI, an architecture that can learn using gradient-descent in
situ is still missing. In this paper, 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 showing a small loss in the performance compared with the
state of the art. We also propose a hardware architecture based on memristive
crossbar arrays to perform the required vector-matrix multiplications. The
necessary peripheral circuitry including pre-synaptic, post-synaptic and write
circuits required for online training, have been designed in the sub-threshold
regime for power saving with a standard 180 nm CMOS process.
Related papers
- 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 Spiking Neural Networks with Local Tandem Learning [96.32026780517097]
Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient than their predecessors.
In this paper, we put forward a generalized learning rule, termed Local Tandem Learning (LTL)
We demonstrate rapid network convergence within five training epochs on the CIFAR-10 dataset while having low computational complexity.
arXiv Detail & Related papers (2022-10-10T10:05:00Z) - Online Training Through Time for Spiking Neural Networks [66.7744060103562]
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models.
Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency.
We propose online training through time (OTTT) for SNNs, which is derived from BPTT to enable forward-in-time learning.
arXiv Detail & Related papers (2022-10-09T07:47:56Z) - Scalable Nanophotonic-Electronic Spiking Neural Networks [3.9918594409417576]
Spiking neural networks (SNN) provide a new computational paradigm capable of highly parallelized, real-time processing.
Photonic devices are ideal for the design of high-bandwidth, parallel architectures matching the SNN computational paradigm.
Co-integrated CMOS and SiPh technologies are well-suited to the design of scalable SNN computing architectures.
arXiv Detail & Related papers (2022-08-28T06:10:06Z) - Training High-Performance Low-Latency Spiking Neural Networks by
Differentiation on Spike Representation [70.75043144299168]
Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware.
It is a challenge to efficiently train SNNs due to their non-differentiability.
We propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance.
arXiv Detail & Related papers (2022-05-01T12:44:49Z) - Online Training of Spiking Recurrent Neural Networks with Phase-Change
Memory Synapses [1.9809266426888898]
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.
arXiv Detail & Related papers (2021-08-04T01:24:17Z) - In-Hardware Learning of Multilayer Spiking Neural Networks on a
Neuromorphic Processor [6.816315761266531]
This work presents a spike-based backpropagation algorithm with biological plausible local update rules and adapts it to fit the constraint in a neuromorphic hardware.
The algorithm is implemented on Intel Loihi chip enabling low power in- hardware supervised online learning of multilayered SNNs for mobile applications.
arXiv Detail & Related papers (2021-05-08T09:22:21Z) - Brain-Inspired Learning on Neuromorphic Substrates [5.279475826661643]
This article provides a mathematical framework for the design of practical online learning algorithms for neuromorphic substrates.
Specifically, we show a direct connection between Real-Time Recurrent Learning (RTRL) and biologically plausible learning rules for training Spiking Neural Networks (SNNs)
We motivate a sparse approximation based on block-diagonal Jacobians, which reduces the algorithm's computational complexity.
arXiv Detail & Related papers (2020-10-22T17:56:59Z) - 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) - Training End-to-End Analog Neural Networks with Equilibrium Propagation [64.0476282000118]
We introduce a principled method to train end-to-end analog neural networks by gradient descent.
We show mathematically that a class of analog neural networks (called nonlinear resistive networks) are energy-based models.
Our work can guide the development of a new generation of ultra-fast, compact and low-power neural networks supporting on-chip learning.
arXiv Detail & Related papers (2020-06-02T23:38:35Z)
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.