Information Coding and Hardware Architecture of Spiking Neural Networks

Abderrahmane, Nassim; Miramond, Benoît

doi:10.1109/dsd.2019.00050

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…In parallel to this observation, different devices have appeared during the last few years which allow the generation of spikes from standard CMOS image sensors (Abderrahmane and Miramond, 2019;Admin, 2020;Spike Event Sensor, 2021). The main objective behind these cameras is, on one hand, to be able to integrate spatial filters upstream of the spikes generation, and on the other hand, to have sensors of different formats going, for example, up to 2M pixels (Caiman Camera, 2021) [usually the pixel-count of event-based cameras is rather limited (Lichtsteiner et al, 2008;Posch et al, 2011;Brandli et al, 2014;Son et al, 2017)].…”

Section: Choice Of the Event Cameramentioning

confidence: 96%

Event-Based Trajectory Prediction Using Spiking Neural Networks

Débat

Chauhan

Cottereau

et al. 2021

Front. Comput. Neurosci.

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In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory by adding a simple read-out layer composed of polynomial regressions, and trained in a supervised manner. Hence, we show that a SNN receiving inputs from an event-based sensor can extract relevant spatio-temporal patterns to process and predict ball trajectories.

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Section: Choice Of the Event Cameramentioning

confidence: 96%

Event-Based Trajectory Prediction Using Spiking Neural Networks

Débat

Chauhan

Cottereau

et al. 2021

Front. Comput. Neurosci.

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“…In contrast, neuronal information in BNNs is transmitted digitally, by single cells that fire asynchronously, as well as in an analogue mode fashion [40,41] making it much slower than modern electronics. The straightforward emulation of the computation paradigm of BNNs is still challenging to achieve because of a clear mismatch between the digital synchronized approach of Von Neumann architectures and the energy-efficient, analogue, and asynchronous communication of the brain [42]. In order to fill this gap new approaches have been developed, including SNNs [43].…”

Section: How Do Machines 'Think'?mentioning

confidence: 99%

From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks

Bruno

Mariano

Rana

et al. 2023

Neuromorph. Comput. Eng.

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The highly efficient computation of the brain relies on its plastic and reconfigurable nature, enabling complex computations and maintenance of vital functions with a remarkably low power consumption of only ~20 W. First efforts to leverage brain-inspired computational principles have led to the introduction of artificial neural networks (ANNs) that revolutionized information processing and daily life. The relentless pursuit of the definitive computing platform is now pushing researchers towards the investigation of novel solutions to emulate specific brain features (such as synaptic plasticity) to allow local and energy efficient computations. The development of such devices may also be pivotal in addressing major challenges of a continuously aging world, including the treatment of neurodegenerative diseases. To date, the neuroelectronics field has been instrumental in deepening our understanding of how neurons exchange information, owing to the rapid development of silicon-based platforms for neural recordings and stimulation. However, this approach still does not allow for in loco processing of the observed biological signals due to two aspects. Firstly, silicon chips applying conventional processing are simply too power hungry to be embedded into the brain. Secondly, despite the success of silicon-based devices in electronic applications, they are ill-suited for directly interfacing with biological tissue. A cornucopia of solutions has therefore been proposed in the last years to obtain neuromorphic materials to create effective biointerfaces and enable reliable bidirectional communication with neurons. Organic conductive materials are not only highly biocompatible and able to electrochemically transduce biological signals, but also promise to include neuromorphic features, such as neuro-transmitter mediated plasticity and learning capabilities. Furthermore, organic electronics, relying on mixed electronic/ionic conduction mechanism, can be efficiently coupled with biological neural networks, while still communicating with silicon-based electronics. We envision neurohybrid systems that integrate silicon-based and organic electronics-based neuromorphic technologies to create active artificial interfaces with biological tissues.

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“…Note that some Rate Coding techniques such as Jittered Periodic apply a random factor to spike emission times, which increases network's prediction accuracy. Among several rate coding techniques presented in a previous work [45], we are using Jittered Periodic method as it reaches the highest performances while not increasing the spiking activity compared to the other rate-based techniques.…”

Section: Rate Codingmentioning

confidence: 99%

Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence

2020

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A B S T R A C TMachine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabilities, thus making them unsuitable for embedded systems. To deal with this limitation, many researchers are investigating brain-inspired computing, which would be a perfect alternative to the conventional Von Neumann architecture based computers (CPU/GPU) that meet the requirements for computing performance, but not for energy-efficiency. Therefore, neuromorphic hardware circuits that are adaptable for both parallel and distributed computations need to be designed. In this paper, we focus on Spiking Neural Networks (SNNs) with a comprehensive study of information coding methods and hardware exploration. In this context, we propose a framework for neuromorphic hardware design space exploration, which allows to define a suitable architecture based on application-specific constraints and starting from a wide variety of possible architectural choices. For this framework, we have developed a behavioral level simulator for neuromorphic hardware architectural exploration named NAXT. Moreover, we propose modified versions of the standard Rate Coding technique to make trade-offs with the Time Coding paradigm, which is characterized by the low number of spikes propagating in the network. Thus, we are able to reduce the number of spikes while keeping the same neuron's model, which results in an SNN with fewer events to process. By doing so, we seek to reduce the amount of power consumed by the hardware. Furthermore, we present three neuromorphic hardware architectures in order to quantitatively study the implementation of SNNs. One of these architectures integrates a novel hybrid structure: a highly-parallel computation core for most solicited layers, and time-multiplexed computation units for deeper layers. These architectures are derived from a novel funnel-like Design Space Exploration framework for neuromorphic hardware.

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Information Coding and Hardware Architecture of Spiking Neural Networks

Cited by 7 publications

References 22 publications

Event-Based Trajectory Prediction Using Spiking Neural Networks

Event-Based Trajectory Prediction Using Spiking Neural Networks

From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks

Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence

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