Deep learning with coherent nanophotonic circuits
Yichen Shen,Nicholas C. Harris,Scott Skirlo,Dirk Englund,Marin Soljacic +4 more
- Vol. 11, Iss: 7, pp 441-446
TLDR
A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.Abstract:
Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.read more
Citations
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Proceedings ArticleDOI
A Design Methodology for Post-Moore’s Law Accelerators: The Case of a Photonic Neuromorphic Processor
TL;DR: This paper presents a design methodology to mitigate this problem by extending high-level hardware-agnostic neural network design tools with functional and performance models of photonic components and shows that adopting this approach enables designers to efficiently navigate the design space and devise hardware-aware systems with alternative technologies.
Posted Content
On the Basis of Brain: Neural-Network-Inspired Change in General Purpose Chips
TL;DR: A simple model formalising the mechanism of demand distribution in the semiconductor industry is constructed, deriving two possible scenarios for chip evolution: the emergence of a new dominant design in the form of a “platform chip” comprising heterogeneous cores and the fragmentation of the industry into submarkets with dedicated chips.
Journal ArticleDOI
Inverse design of photonic nanostructures using dimensionality reduction: reducing the computational complexity.
TL;DR: In this paper, a deep learning-based method using neural networks (NNs) for inverse design of photonic nanostructures is presented. But this method is limited to the design of thin-film structures composed of consecutive layers of different dielectrics.
Book ChapterDOI
A Robust, Quantization-Aware Training Method for Photonic Neural Networks
A. Oikonomou,Manos Kirtas,N. Passalis,George Mourgias-Alexandris,Miltiadis Moralis-Pegios,Nikos Pleros,Anastasios Tefas +6 more
TL;DR: In this article , the authors proposed a novel training method that is able to compensate for quantization noise, which profoundly exists in photonic hardware due to analog-to-digital (ADC) and digital-toanalog (DAC) conversions.
Journal ArticleDOI
Optical and electrical programmable computing energy use comparison.
TL;DR: Optical computing has been proposed as a replacement for electrical computing to reduce energy use of math intensive programmable applications like machine learning, but it is found that energy use is dominated by data transfer, and that computingEnergy use is a small fraction of the total.
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