A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

Fang, Yan; Wang, Zheng; Gomez, Jorge; Datta, Suman; Khan, Asif Islam; Raychowdhury, Arijit

doi:10.3389/fnins.2019.00855

Cited by 24 publications

(15 citation statements)

References 56 publications

(80 reference statements)

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“…While PCM and ReRAM technologies have been widely used in neuromorphic systems in the past years [78], ferroelectric technology has only recently been investigated for these applications [15]. The systems reported are mainly at simulation level, for both FTJs [34,35,37] and FeFETs [57,[79][80][81][82].…”

Section: Discussionmentioning

confidence: 99%

Ferroelectric-based synapses and neurons for neuromorphic computing

Covi

Mulaosmanovic

Max

et al. 2022

Neuromorph. Comput. Eng.

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The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as Spiking Neural Networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems' power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technology for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.

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Section: Discussionmentioning

confidence: 99%

Ferroelectric-based synapses and neurons for neuromorphic computing

Covi

Mulaosmanovic

Max

et al. 2022

Neuromorph. Comput. Eng.

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“…Recently, Fang et al demonstrated that certain optimization problems could be solved driven by the coupled dynamics of ferroelectric ﬁeld‐effect transistor (FeFET)‐based spiking neurons. [ 84 ] While there was no synaptic weight adaptation in this approach, the optimal solution to the problem is determined by the coupled interactions between the neurons, which modulate each other's membrane potentials in an event‐driven manner.…”

Section: Snns and Memristorsmentioning

confidence: 99%

Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing

Mehonić¹,

Sebastian

Rajendran

et al. 2020

Advanced Intelligent Systems

183

121

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Three factors are currently driving the main developments in artificial intelligence (AI): availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. Graphics processing units (GPUs) have been demonstrated as effective co-processors for the implementation of machine learning (ML) algorithms based on deep learning (DL). Solutions based on DL and GPU implementations have led to massive improvements in many AI tasks, but have also caused an exponential increase in demand for computing power. Recent analyses show that the demand for computing power has increased by a factor of 300 000 since 2012, and the estimate is that this demand will double every 3.4 months-at a much faster rate than improvements made historically through Moore's scaling (a sevenfold improvement over the same period of time). [1] At the same time, Moore's law has been slowing down significantly for the last few years, [2] as there are strong indications that we will not be able to continue scaling down complementary metal-oxide-semiconductor (CMOS) transistors. This calls for the exploration of alternative technology roadmaps for the development of scalable and efficient AI solutions. Transistor scaling is not the only way to improve computing performance. Architectural innovations, such as GPUs, field-programmable arrays (FPGAs), and application-specific integrated circuits (ASICs), have all significantly advanced the ML field. [3] A common aspect of modern computing architectures for ML is a move away from the classical von-Neumann architecture that physically separates memory and computing. This approach yields a performance bottleneck that is often the main reason for both energy and speed inefficiency of ML implementations on conventional hardware platforms due to

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“…Especially, Spiking Neural Networks (SNNs) process binary spikes through time like a human brain, resulting in 1-2 order of magnitude energy efficiency over ANNs on emerging neuromorphic hardware [42,11,1,4]. Due to the energy advantages and neuroscientific interest, SNNs have made great strides on various applications such as image recognition [31,26,6], visualization [27], optimization [8,9], and object detection [24]. Therefore, SNNs have a huge potential to be exploited on real-world edge devices.…”

Section: Introductionmentioning

confidence: 99%

PrivateSNN: Privacy-Preserving Spiking Neural Networks

Kim¹,

Venkatesha²,

Panda³

2021

Preprint

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How can we bring both privacy and energy-efficiency to a neural system on edge devices? In this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural Networks (SNNs) from a pre-trained ANN model without leaking sensitive information contained in a dataset. Here, we tackle two types of leakage problems: 1) Data leakage caused when the networks access real training data during an ANN-SNN conversion process. 2) Class leakage is the concept of leakage caused when class-related features can be reconstructed from network parameters. In order to address the data leakage issue, we generate synthetic images from the pre-trained ANNs and convert ANNs to SNNs using generated images. However, converted SNNs are still vulnerable with respect to the class leakage since the weight parameters have the same (or scaled) value with respect to ANN parameters. Therefore, we encrypt SNN weights by training SNNs with a temporal spike-based learning rule. Updating weight parameters with temporal data makes networks difficult to be interpreted in the spatial domain. We observe that the encrypted PrivateSNN can be implemented not only without the huge performance drop (less than ∼5%) but also with significant energy-efficiency gain (about ×60 compared to the standard ANN). We conduct extensive experiments on various datasets including CIFAR10, CIFAR100, and TinyImageNet, highlighting the importance of privacy-preserving SNN training.

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A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

Cited by 24 publications

References 56 publications

Ferroelectric-based synapses and neurons for neuromorphic computing

Ferroelectric-based synapses and neurons for neuromorphic computing

Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing

PrivateSNN: Privacy-Preserving Spiking Neural Networks

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