A system design perspective on neuromorphic computer processors

Rose, Garrett S.; Shawkat, Mst Shamim Ara; Foshie, Adam Z.; Murray, J.; Adnan, Musabbir

doi:10.1088/2634-4386/ac24f5

Cited by 9 publications

(6 citation statements)

References 88 publications

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“…The human neural system 368 including the brain receives the perception from the environment and executes the decision making, predicting, and actuating. The human brain collects the neural pulses from receptor cells for the five senses, generates the perception, and transmits commands to executive organs, involving stretch muscles, for response to the physical world.…”

Section: ■ Artificial Brain For Data Processing and Decision Makingmentioning

confidence: 99%

Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles

et al. 2023

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Graphene remains of great interest in biomedical applications because of biocompatibility. Diseases relating to human senses interfere with life satisfaction and happiness. Therefore, the restoration by artificial organs or sensory devices may bring a bright future by the recovery of senses in patients. In this review, we update the most recent progress in graphene based sensors for mimicking human senses such as artificial retina for image sensors, artificial eardrums, gas sensors, chemical sensors, and tactile sensors. The brain-like processors are discussed based on conventional transistors as well as memristor related neuromorphic computing. The brain−machine interface is introduced for providing a single pathway. Besides, the artificial muscles based on graphene are summarized in the means of actuators in order to react to the physical world. Future opportunities remain for elevating the performances of human-like sensors and their clinical applications.

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Section: ■ Artificial Brain For Data Processing and Decision Makingmentioning

confidence: 99%

Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles

et al. 2023

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“…The use of memristors has become popular in neuromorphic circuits since synaptic weight can be encoded with a memristance value. Additionally, two terminal memristive synapse consumes less power, area, and cost [37], [38].…”

Section: ) Synapse Using Memristor and Input Neuronmentioning

confidence: 99%

A Single Chip SPAD Based Vision Sensing System With Integrated Memristive Spiking Neuromorphic Processing

et al. 2023

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This paper presents a new scalable 8 × 8 single photon avalanche diode (SPAD) based vision sensor with integrated spiking neuromorphic system on a single chip. The proposed vision sensing system adopts the benefits of SPAD's high quantum efficiency and energy efficiency of memristive spiking neuromorphic processing. The SPAD based vision sensor includes biologically inspired address event representation (AER) readout to generate asynchronous digital address events at the output reducing computation and making it suitable to process directly with integrated on-chip spiking neuromorphic system in a faster and more energy efficient way. A novel on-chip interface is designed to convert the output events of a SPAD-based event sensor into temporally coded spikes (TCS) that enable on-chip processing with integrated spiking neuromorphic system. We have tested the prototype vision sensing system for imaging characters by SPAD based vision sensor and recognizing them using the integrated memristive spiking neuromorphic system. To help with the evaluation, we have built a complete temporal pulses data set from simulating the SPAD vision sensor with AER readout in imaging characters and applied directly to integrated spiking neuromorphic system via designed novel on-chip interface. The achieved accuracy is 89.54% with a power consumption of 316 𝜇W for the memristive neuromorphic processor. The SPAD based vision sensor exhibits array-level dynamic range of 148 dB with a power consumption of 2.8 mW. The designed SPAD-based vision sensing system with an integrated spiking neuromorphic system on a single chip shows great promise for robotics, autonomous vehicles, health, and security applications.

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“…Hybrid analog-digital systems emulating spiking neurons were also developed as an alternative to purely analog models [2]. Since then, neuromorphic computers have evolved to further emulate the computational architectures of neurons and of functional networks of neurons (for recent reviews, see [3][4][5][6][7][8][9][10]).…”

Section: Introductionmentioning

confidence: 99%

The free energy principle induces neuromorphic development

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Glazebrook

et al. 2022

Neuromorph. Comput. Eng.

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We show how any finite physical system with morphological, i.e. three-dimensional embedding or shape, degrees of freedom and locally limited free energy will, under the constraints of the free energy principle, evolve over time towards a neuromorphic morphology that supports hierarchical computations in which each “level” of the hierarchy enacts a coarse-graining of its inputs, and dually, a fine-graining of its outputs. Such hierarchies occur throughout biology, from the architectures of intracellular signal transduction pathways to the large-scale organization of perception and action cycles in the mammalian brain. Formally, the close formal connections between cone-cocone diagrams (CCCD) as models of quantum reference frames on the one hand, and between CCCDs and topological quantum field theories on the other, allow the representation of such computations in the fully-general quantum-computational framework of topological quantum neural networks.

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A system design perspective on neuromorphic computer processors

Cited by 9 publications

References 88 publications

Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles

Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles

A Single Chip SPAD Based Vision Sensing System With Integrated Memristive Spiking Neuromorphic Processing

The free energy principle induces neuromorphic development

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