Probabilistic inference using stochastic spiking neural networks on a neurosynaptic processor

Ahmed, Khadeer; Shrestha, Amar; Qiu, Qinru; Wu, Qing

doi:10.1109/ijcnn.2016.7727759

Cited by 18 publications

(18 citation statements)

References 20 publications

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“…Variants of this approach have been applied to spiking neural networks (eg. [1,13,19]). These are mostly orthogonal to the ideas we discuss, since a different (stochastic) hardware architecture for individual compute units can also be incorporated into our approach.…”

Section: Stochastic Techniquesmentioning

confidence: 99%

Probabilistic spike propagation for FPGA implementation of spiking neural networks

Nallathambi

Chandrachoodan

2020

Preprint

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Section: Stochastic Techniquesmentioning

confidence: 99%

Probabilistic spike propagation for FPGA implementation of spiking neural networks

Nallathambi

Chandrachoodan

2020

Preprint

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“…We utilize a simplified version [32] of the neuron model proposed in [33]. Here the membrane potential u(t) of neuron Z is computed as…”

Section: Neuron Model and Learning Rulementioning

confidence: 99%

“…An integrate and fire neuron Z spikes when the membrane potential crosses the threshold and then its membrane potential is reset to 0. When the threshold is set to be random over a specified range, the stochastic integrate-and-fire neuron (SIF) approximates the Bayesian neuron in [32]. In order to aggregate or relay spike activities, we also introduce spiking Rectified Linear Unit (ReLU) neuron.…”

Section: Neuron Model and Learning Rulementioning

confidence: 99%

“…To validate the functioning of the hardware design, we employ a simple pattern learning task. In this task, we utilize a simple winner-take-all (WTA) circuit to learn handwritten digits 0 and 1 [32] from MNIST data set. The network is trained using 100 samples and each sample is exposed to the network for 100 NECs.…”

Section: A Unsupervised Learning Of Mnist Digitsmentioning

confidence: 99%

“…The first layer consists of 196 average pooling neurons whose fanin is 2x2, and the second layer consists of 4 SIF neurons with STDP learning enabled and 4 ReLU neurons to relay the spikes. These 8 neurons form a winner-takes-all (WTA) circuit as described in [32]. The 196 neurons in the first layer are mapped to 14 cores and all neurons in the second layer are mapped 1 core.…”

Section: A Unsupervised Learning Of Mnist Digitsmentioning

confidence: 99%

See 2 more Smart Citations

Scalable NoC-based Neuromorphic Hardware Learning and Inference

Fang

Shrestha

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Bio-inspired neuromorphic hardware is a research direction to approach brain's computational power and energy efficiency. Spiking neural networks (SNN) encode information as sparsely distributed spike trains and employ spike-timingdependent plasticity (STDP) mechanism for learning. Existing hardware implementations of SNN are limited in scale or do not have in-hardware learning capability. In this work, we propose a low-cost scalable Network-on-Chip (NoC) based SNN hardware architecture with fully distributed in-hardware STDP learning capability. All hardware neurons work in parallel and communicate through the NoC. This enables chip-level interconnection, scalability and reconfigurability necessary for deploying different applications. The hardware is applied to learn MNIST digits as an evaluation of its learning capability. We explore the design space to study the trade-offs between speed, area and energy. How to use this procedure to find optimal architecture configuration is also discussed.

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Text classification in memristor-based spiking neural networks

Huang¹,

Serb²,

Stathopoulos³

et al. 2023

Neuromorph. Comput. Eng.

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Memristors, emerging non-volatile memory devices, have shown promising potential in neuromorphic hardware designs, especially in spiking neural network (SNN) hardware implementation. Memristor-based SNNs have been successfully applied in a wide range of applications, including image classification and pattern recognition. However, implementing memristor-based SNNs in text classification is still under exploration. One of the main reasons is that training memristor-based SNNs for text classification is costly due to the lack of efficient learning rules and memristor non-idealities. To address these issues and accelerate the research of exploring memristor-based spiking neural networks in text classification applications, we develop a simulation framework with a virtual memristor array using an empirical memristor model. We use this framework to demonstrate a sentiment analysis task in the IMDB movie reviews dataset. We take two approaches to obtain trained spiking neural networks with memristor models: 1) by converting a pre-trained artificial neural network (ANN) to a memristor-based SNN, or 2) by training a memristor-based SNN directly. These two approaches can be applied in two scenarios: offline classification and online training. We achieve the classification accuracy of 85.88\% by converting a pre-trained ANN to a memristor-based SNN and 84.86\% by training the memristor-based SNN directly, given that the baseline training accuracy of the equivalent ANN is 86.02\%. We conclude that it is possible to achieve similar classification accuracy in simulation from ANNs to SNNs and from non-memristive synapses to data-driven memristive synapses. We also investigate how global parameters such as spike train length, the read noise, and the weight updating stop conditions affect the neural networks in both approaches. This investigation further indicates that the simulation using statistic memristor models in the two approaches presented by this paper can assist the exploration of memristor-based SNNs in natural language processing tasks.

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Probabilistic inference using stochastic spiking neural networks on a neurosynaptic processor

Cited by 18 publications

References 20 publications

Probabilistic spike propagation for FPGA implementation of spiking neural networks

Probabilistic spike propagation for FPGA implementation of spiking neural networks

Scalable NoC-based Neuromorphic Hardware Learning and Inference

Text classification in memristor-based spiking neural networks

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