System Design for In-Hardware STDP Learning and Spiking Based Probablistic Inference

Ahmed, Khadeer; Shrestha, Amar; Wang, Yanzhi; Qiu, Qinru

doi:10.1109/isvlsi.2016.91

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…Other simulators capable of modeling software based models and models for custom neuromorphic hardware are presented in [20,57,58,59]. This is still an ongoing field of research and there are several more accelerator-based simulators available hence the reader is encouraged to explore further.…”

Section: Snn Simulation Tools and Hardware Acceleratorsmentioning

confidence: 99%

“…Some of the text and images are adopted from the available research literature. Rest of the work represents authors original contributions along with the co-authors of the following research contributions [20,37,38,42,59,61]. I am thankful for the support of Dr. Qinru Qiu from Syracuse University and her research group members specifically Amar Shrestha in contributing during the original research.…”

Section: Acknowledgementsmentioning

confidence: 99%

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Brain-Inspired Spiking Neural Networks

Ahmed¹

2021

Biomimetics

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Brain is a very efficient computing system. It performs very complex tasks while occupying about 2 liters of volume and consuming very little energy. The computation tasks are performed by special cells in the brain called neurons. They compute using electrical pulses and exchange information between them through chemicals called neurotransmitters. With this as inspiration, there are several compute models which exist today trying to exploit the inherent efficiencies demonstrated by nature. The compute models representing spiking neural networks (SNNs) are biologically plausible, hence are used to study and understand the workings of brain and nervous system. More importantly, they are used to solve a wide variety of problems in the field of artificial intelligence (AI). They are uniquely suited to model temporal and spatio-temporal data paradigms. This chapter explores the fundamental concepts of SNNs, few of the popular neuron models, how the information is represented, learning methodologies, and state of the art platforms for implementing and evaluating SNNs along with a discussion on their applications and broader role in the field of AI and data networks.

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Section: Snn Simulation Tools and Hardware Acceleratorsmentioning

confidence: 99%

Section: Acknowledgementsmentioning

confidence: 99%

Brain-Inspired Spiking Neural Networks

Ahmed¹

2021

Biomimetics

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“…An extremely common use of both neural networks and neuromorphic implementations has been on various image-based applications, including edge detection [220], [339], [520], [783], [829], [922], [1260], [1873], [2107], [2118], [2141], [2287], [2564], image compression [641], [721], [875], [960], [1243], [1263], image filtering [15], [338], [1112], [1255], [1267], [1492], [1516], [1551], [1779], [1886], [1887], [2565], [2566], image segmentation [141], [490], [541], [921], [1272]- [1274], [1277], [1278], [1679], [1712], [1713], [2567]- [2569], and feature extraction [388],…”

Section: Applicationsmentioning

confidence: 99%

“…Neuromorphic systems have also been applied to natural language processing (NLP) tasks, many of which require recurrent networks. Example applications in NLP that have been demonstrated using neuromorphic systems include sentence construction [388], sentence completion [1097], [2411], [2637], question subject classification [581], sentiment analysis [583], and document similarity [1239].…”

Section: Applicationsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

175

180

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Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brainlike ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

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“…Hardware implementations of STDP learning [21] [22] focus more on circuit and device level analysis to achieve variable synaptic plasticity instead of scalability. [23] proposed a digital hardware neuron model for synaptic plasticity, it focuses on the design of individual neuron cores, interconnection and scalability are not addressed. Few analog VLSI approaches of synaptic plasticity are proposed in [24][25] [26], which focus on the individual synapses design without addressing large scale network implementation and architectural design.…”

Section: Related Workmentioning

confidence: 99%

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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System Design for In-Hardware STDP Learning and Spiking Based Probablistic Inference

Cited by 8 publications

References 24 publications

Brain-Inspired Spiking Neural Networks

Brain-Inspired Spiking Neural Networks

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Scalable NoC-based Neuromorphic Hardware Learning and Inference

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