Primer on silicon neuromorphic photonic processors: architecture and compiler

Nanophotonics

article

Thomas Ferreira de Lima¹  Volker J. Sorger²  Bhavin J. Shastri¹  Paul R. Prucnal¹  Alexander N. Tait¹  Armin Mehrabian²  Mitchell A. Nahmias¹  Chaoran Huang¹  Hsuan-Tung Peng¹  Bicky A. Marquez³  Mario Miscuglio²  Tarek El-Ghazawi² 

[1] Department of Electrical Engineering, Princeton University;Department of Electrical and Computer Engineering, George Washington University;Department of Physics, Engineering Physics & Astronomy, Queen’s University

关键词: neuromorphic computing;    optical neural networks;    photonic integrated circuits;    silicon photonics;    ultrafast information processing;   

DOI  :  10.1515/nanoph-2020-0172

学科分类：社会科学、人文和艺术（综合）

来源: De Gruyter

【 摘 要 】

Microelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.

【 授权许可】

CC BY