A programming framework for neuromorphic systems with emerging technologies

Schuman, Catherine D.; Plank, James S.; Rose, Garrett S.; Chakma, Gangotree; Wyer, Austin; Bruer, Grant; Laanait, Nouamane

doi:10.1145/3109453.3123958

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…To realize the spiking neural functionalities by utilizing the leakyintegrate magnetization dynamics [17]. To enable the abstraction of the magnetic functions as stochastic spiking neurons, the parameter required is the thermal noise which is prevalent in nano-magnets at certain temperatures which are not equal to zero [18]. The reconfigurable neuron model consists of two units such as finite state machine unit (FSM) unit and communication unit.…”

Section: Algorithm Designmentioning

confidence: 99%

Design and Implementation of Reconfigurable Neuro-Inspired Computing Model on a FPGA

Prashanth¹,

Ahmed²

2020

Adv. sci. technol. eng. syst. j.

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In this paper we design a large scale reconfigurable digital bio-inspired computing model. We consider the reconfigurable and event driven parameters in the developed fieldprogrammable neuromorphic computing system. The various Intellectual Property (IP) cores are developed for the modules such as Block RAM, Differential Clock, Floating Point, and First In First Out (FIFO) for the design of the neuron model in Xilinx ISE, with exploration of register transfer logic (RTL) and hardware synthesis using Verilog code. The architecture for design at device level offers the best possible design tradeoff for specific processor architectures and development choices. In this paper we perform algorithmic design of a large scale reconfigurable logical bio-inspired computing model. The proposed algorithm is implemented on Field Programmable Gate Array (FPGA) to develop a neuron model to be utilized in neuromorphic computing system.

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Section: Algorithm Designmentioning

confidence: 99%

Design and Implementation of Reconfigurable Neuro-Inspired Computing Model on a FPGA

Prashanth¹,

Ahmed²

2020

Adv. sci. technol. eng. syst. j.

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“…This remarkable thumb-rule of onchip shrinking can be applied only for the system board [3]. The emergence of a system in package and need of analog functionalities such as RF communication, passive, MEMS has spurred computing devices and material communities into a flurry of activity in the creation of beyond CMOS and Beyond Von-Neumann; and also questioned the authenticity of Moore's law [4]. The answer is the potential of spin transfer torque devices as an alternative to complementary metal oxide semiconductor for Von-Neumann and non-boolean computing.…”

Section: Introductionmentioning

confidence: 99%

Advent of Spintronics in Neuromorphic Computing Systems: A Study

Ahmed¹

2022

Preprint

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<p>In nature the crucial role of learning and memory is achieved with the help of synapses. The synapses which are plastic in nature, is formed with inter cellular connections of neurons, and the combination of these biological structures form the basic building blocks of neural networks. Synapses can change their state based on the neural activity of coupled neurons. The functionality of neurons and synapses is mimicked in hardware by utilizing very large scale integration technology, plays a key role in design of neuromorphic computing systems. The pathway to efficient neuromorphic computing systems is encoding the neural and synaptic functionalities in an electronic spin. One dominant technology that consistently features in the list of neuromorphic computing systems is spintronics (with the spin behavior of electrons). It shows the potentials to exploit energy efficiency, performance,reliability, magnetization using electric fields, and enhanced memory density of spintronics memory devices. This article provides a high level survey of the beyond Complementary Metal Oxide Semiconductor(CMOS) research with an emphasis on spintronic devices and focussed review on neuromorphic computing based on magnetic skyrmions. Here we review the latest progress in the domain of spintronic neuromorphic computing with the coherent presentation of ideas is provided. The survey stretch the wide-ranging expanse of spintronics-based neuromorphic computing systems, and the scheme is to point out current trends, evolving research issues and further directions on spintronics devices deployed on neuromorphic computing systems. We commence the discussion with a historical perspective, evolution, and subsequently, we make an in-depth survey of underlying components.</p>

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FPGA Implementation of Bio-Inspired Computing Architecture Based on Simple Neuron Model

Prashanth

Ahmed

2020

2020 International Conference on Artificial Intelligence and Signal Processing (AISP)

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A programming framework for neuromorphic systems with emerging technologies

Cited by 5 publications

References 39 publications

Design and Implementation of Reconfigurable Neuro-Inspired Computing Model on a FPGA

Design and Implementation of Reconfigurable Neuro-Inspired Computing Model on a FPGA

Advent of Spintronics in Neuromorphic Computing Systems: A Study

FPGA Implementation of Bio-Inspired Computing Architecture Based on Simple Neuron Model

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