Photonic Multiply-Accumulate Operations for Neural Networks

• Published in: IEEE journal of selected topics in quantum electronics Vol. 26; no. 1; pp. 1 - 18
• Main Authors: Nahmias, Mitchell A., de Lima, Thomas Ferreira, Tait, Alexander N., Peng, Hsuan-Tung, Shastri, Bhavin J., Prucnal, Paul R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-01-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: analog computers; analog processing circuits; Artificial intelligence; Computational modeling; Deep learning; Digital electronics; Hardware; Low noise; Machine learning; Mathematical analysis; Matrix methods; Metals; Microprocessors; Neural networks; optical computing; Photonics; Processors; Program processors; Training
• ISSN: 1077-260X; 1558-4542
• DOI: 10.1109/JSTQE.2019.2941485

It has long been known that photonic communication can alleviate the data movement bottlenecks that plague conventional microelectronic processors. More recently, there has also been interest in its capabilities to implement low precision linear operations, such as matrix multiplications, fast and efficiently. We characterize the performance of photonic and electronic hardware underlying neural network models using multiply-accumulate operations. First, we investigate the limits of analog electronic crossbar arrays and on-chip photonic linear computing systems. Photonic processors are shown to have advantages in the limit of large processor sizes (>100 μm), large vector sizes (N > 500), and low noise precision (≤4 bits). We discuss several proposed tunable photonic MAC systems, and provide a concrete comparison between deep learning and photonic hardware using several empiricallyvalidated device and system models. We show significant potential improvements over digital electronics in energy (>10 2 ), speed (>10 3 ), and compute density (>10 2 ).