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Photonic Multiply-Accumulate Operations for Neural Networks
- Source :
- IEEE Journal of Selected Topics in Quantum Electronics. 26:1-18
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- 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 ( ${>}\text{100}\; \mu$ m), large vector sizes ( $N > 500)$ , and low noise precision ( ${\leq} 4$ bits). We discuss several proposed tunable photonic MAC systems, and provide a concrete comparison between deep learning and photonic hardware using several empirically-validated device and system models. We show significant potential improvements over digital electronics in energy ( ${>}10^2$ ), speed ( ${>}10^3$ ), and compute density ( ${>}10^2$ ).
- Subjects :
- Digital electronics
business.industry
Computer science
Analog computer
Optical computing
02 engineering and technology
021001 nanoscience & nanotechnology
Atomic and Molecular Physics, and Optics
Matrix multiplication
Computational science
law.invention
020210 optoelectronics & photonics
law
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Crossbar switch
Photonics
0210 nano-technology
Electronic hardware
business
Energy (signal processing)
Subjects
Details
- ISSN :
- 15584542 and 1077260X
- Volume :
- 26
- Database :
- OpenAIRE
- Journal :
- IEEE Journal of Selected Topics in Quantum Electronics
- Accession number :
- edsair.doi...........24c452f9214e8e8da32b84b92aa4622d