Spiking neural networks — Algorithms, hardware implementations and applications

Kulkarni, Shruti; Babu, Anakha V; Rajendran, Bipin

doi:10.1109/mwscas.2017.8052951

Cited by 13 publications

(4 citation statements)

References 56 publications

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“…SNNs require equal synaptic sign transmission and weight update and actualizing them on CPUs that are innately sequential limits their speed [30]. SNNs can be implemented in VLSI systems [7].…”

Section: Snn Implementationmentioning

confidence: 99%

A Brief Review on Spiking Neural Network - A Biological Inspiration<b> </b>

Th¹

2021

Preprint

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Recent advancement of deep learning has been elevated the multifaceted nature in various applications of this field. Artificial neural networks are now turning into a genuinely old procedure in the vast area of computer science; the principal thoughts and models are more than fifty years of age. However, in this modern computing era, 3rd generation intelligent models are introduced by scientists. In the biological neuron, actual film channels control the progression of particles over the layer by opening and shutting in light of voltage changes because of inborn current flows and remotely led to signals. A comprehensive 3rd generation, Spiking Neural Network (SNN) is diminishing the distance between deep learning, machine learning, and neuroscience in a biologically-inspired manner. It also connects neuroscience and machine learning to establish high-level efficient computing. Spiking Neural Networks initiate utilizing spikes, which are discrete functions that happen at focuses as expected, as opposed to constant values. This paper is a review of the biological-inspired spiking neural network and its applications in different areas. The author aims to present a brief introduction to SNN, which incorporates the mathematical structure, applications, and implementation of SNN. This paper also represents an overview of machine learning, deep learning, and reinforcement learning. This review paper can help advanced artificial intelligence researchers to get a compact brief intuition of spiking neural networks.

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Section: Snn Implementationmentioning

confidence: 99%

A Brief Review on Spiking Neural Network - A Biological Inspiration<b> </b>

Th¹

2021

Preprint

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“…(2). Software simulation is a common way to implement SNNs, but for complex SNNs, computational cost greatly increases the processing time and affects the real-time performance of the networks (Brette et al, 2007 ; Shayani et al, 2008 ; Kulkarni et al, 2017 ). Besides, neuroanimats need to explore and learn in the real world, so their power consumption is also needing to be considered.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance

et al. 2022

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Spiking Neural Networks (SNNs) are often considered the third generation of Artificial Neural Networks (ANNs), owing to their high information processing capability and the accurate simulation of biological neural network behaviors. Though the research for SNNs has been quite active in recent years, there are still some challenges to applying SNNs to various potential applications, especially for robot control. In this study, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario.

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“…The human brain can carry out computations in a power efficient and immensely parallel manner which has motivated the trend in bio-inspired computing [1]. Spiking Neural Networks (SNNs) are a popular bio-inspired paradigm that have been used in many applications [2]. The self-repairing ability of the human brain is a key attractive feature that engineers are keen to implement in the next generation of computers.…”

Section: Introductionmentioning

confidence: 99%

AstroByte: Multi-FPGA Architecture for Accelerated Simulations of Spiking Astrocyte Neural Networks

Karim

Harkin

McDaid

et al. 2020

2020 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Spiking astrocyte neural networks (SANN) are a new computational paradigm that exhibit enhanced self-adapting and reliability properties. The inclusion of astrocyte behaviour increases the computational load and critically the number of connections, where each astrocyte typically communicates with 6-9 neurons (and their associated synapses) with feedback pathways from each neuron to the astrocyte. Each astrocyte cell also communicates with its neighbouring cell resulting in a significant interconnect density. The substantial level of parallelisms in SANNs lends itself to acceleration in hardware, however, the challenge in accelerating simulations of SANNs firmly resides in scalable interconnect and the ability to inject and retrieve data from the hardware. This paper presents a novel multi-FPGA acceleration architecture, AstroByte, for the speedup of SANNs. AstroByte explores Networks-on-Chip (NoC) routing mechanisms to address the challenge of communicating both spike event (neuron data) and numeric (astrocyte data) across significant interconnect pathways between astrocytes and neurons. AstroByte also exploits the NoC interconnect to inject data and retrieve runtime data from the accelerated SANN simulations. Results will show that AstroByte can simulate SANN applications with speedup factors of between x162-x188 over Matlab equivalent simulations.

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Spiking neural networks — Algorithms, hardware implementations and applications

Cited by 13 publications

References 56 publications

A Brief Review on Spiking Neural Network - A Biological Inspiration<b> </b>

A Brief Review on Spiking Neural Network - A Biological Inspiration<b> </b>

Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance

AstroByte: Multi-FPGA Architecture for Accelerated Simulations of Spiking Astrocyte Neural Networks

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