Machine learning and artificial intelligence (AI) is becoming a ubiquitous technology of the looming industry-4.0 era. However, progress of adopting intelligent automation of systems is limited by hardware overhead such as throughput, power consumption, and latency. At a conceptual level, electronics is at the end of its scaling law and alternative accelerators are sought after. Optical co-processors offer a high-degree of algorithmic homomorphism to implement general matrix-matrix multiplication operations via on-the-fly multiplication performed by electro-optic components, and accumulation operations performed by photodetectors. However, recent emerging photonic AI engines are cumbersome to program, follow overhead-heavy scaling laws, or rely on discretely-packaged photonic components, all reducing performance. Here we introduce a photonic tensor core processor featuring a complete chip-integrated multiply-accumulate engine on the photonic circuits, signal parallelism via wavelength division multiplexing, and tunable 5-bit electronic programmable weights, all integrated into a small-formfactor silicon photonics platform. This approach shows a high throughput-efficiency AI engine (0.21 TOPS/W). Using this stand-alone hybrid photonic-electronic machine learning accelerator, we demonstrate versatile applications including low-error image filtering and edge detection, augmented reality, and high-accuracy machine learning image classification tasks. This hybrid electronic-photonic tensor processor offers a versatile low-overhead and compact formfactor solution for extreme edge AI applications, but also can be utilized for in-cloud machine learning training tasks.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.