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  1. Free-space optical spiking neural network
Free-space optical spiking neural network
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Free-space optical spiking neural network

Ahmadi, R

Free-space optical spiking neural network

Ahmadi, R ; Sharif University of Technology | 2024

53 Viewed
  1. Type of Document: Article
  2. DOI: 10.1371/journal.pone.0313547
  3. Publisher: 2024
  4. Abstract:
  5. Neuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. As an alternative, optical implementations of such processors have been proposed, capitalizing on the intrinsic information-processing capabilities of light. Among the various Optical Neural Networks (ONNs) explored within the realm of optical neuromorphic engineering, Spiking Neural Networks (SNNs) have exhibited notable success in emulating the computational principles of the human brain. The event-based spiking nature of optical SNNs offers capabilities in low-power operation, speed, temporal processing, analog computing, and hardware efficiency that are difficult or impossible to match with other ONN types. In this work, we introduce the pioneering Free-space Optical Deep Spiking Convolutional Neural Network (OSCNN), a novel approach inspired by the computational model of the human eye. Our OSCNN leverages free-space optics to enhance power efficiency and processing speed while maintaining high accuracy in pattern detection. Specifically, our model employs Gabor filters in the initial layer for effective feature extraction, and utilizes optical components such as Intensity-to-Delay conversion and a synchronizer, designed using readily available optical components. The OSCNN was rigorously tested on benchmark datasets, including MNIST, ETH80, and Caltech, demonstrating competitive classification accuracy. Our comparative analysis reveals that the OSCNN consumes only 1.6 W of power with a processing speed of 2.44 ms, significantly outperforming conventional electronic CNNs on GPUs, which typically consume 150-300 W with processing speeds of 1-5 ms, and competing favorably with other free-space ONNs. Our contributions include addressing several key challenges in optical neural network implementation. To ensure nanometer-scale precision in component alignment, we propose advanced micro-positioning systems and active feedback control mechanisms. To enhance signal integrity, we employ high-quality optical components, error correction algorithms, adaptive optics, and noise-resistant coding schemes. The integration of optical and electronic components is optimized through the design of high-speed opto-electronic converters, custom integrated circuits, and advanced packaging techniques. Moreover, we utilize highly efficient, compact semiconductor laser diodes and develop novel cooling strategies to minimize power consumption and footprint. © 2024 Ahmadi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
  6. Keywords:
  7. Algorithms ; Brain ; Humans ; Models, Neurological ; Artificial neural network ; Convolutional neural network ; Cooling ; Error ; Feature extraction ; Machine learning ; Noise ; Optics ; Packaging ; Processing speed ; Receptive field ; Simulation ; Spiking neural network ; Action potential ; Biological model ; Physiology
  8. Source: PLoS ONE ; Volume 19, Issue 12 December , 2024 ; 19326203 (ISSN)
  9. URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0313547