Parallel convolutional processing using an integrated photonic tensor core
Autor: | Nathan Youngblood, Anton Lukashchuk, Abu Sebastian, Maxim Karpov, M. Le Gallo, Tobias J. Kippenberg, Wolfram H. P. Pernice, Johannes Feldmann, Harish Bhaskaran, C.D. Wright, Arslan S. Raja, Maik Stappers, Helge Gehring, Junqiu Liu, Xin Fu, Xuan Li |
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Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
Computer science Computer Science - Emerging Technologies Physics::Optics FOS: Physical sciences Information technology 02 engineering and technology Integrated circuit 01 natural sciences 7. Clean energy law.invention Application-specific integrated circuit law 0103 physical sciences Electronic engineering Frequency combs 010302 applied physics Nanophotonics and plasmonics Multidisciplinary business.industry Bandwidth (signal processing) Disordered Systems and Neural Networks (cond-mat.dis-nn) Condensed Matter - Disordered Systems and Neural Networks 021001 nanoscience & nanotechnology Emerging Technologies (cs.ET) CMOS Transmission (telecommunications) visual_art Electronic component visual_art.visual_art_medium Hardware acceleration Photonics 0210 nano-technology business Physics - Optics Optics (physics.optics) |
Zdroj: | Nature |
ISSN: | 1476-4687 0028-0836 |
Popis: | With the proliferation of ultrahigh-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence (AI)(1), the world is generating exponentially increasing amounts of data that need to be processed in a fast and efficient way. Highly parallelized, fast and scalable hardware is therefore becoming progressively more important(2). Here we demonstrate a computationally specific integrated photonic hardware accelerator (tensor core) that is capable of operating at speeds of trillions of multiply-accumulate operations per second (10(12) MAC operations per second or tera-MACs per second). The tensor core can be considered as the optical analogue of an application-specific integrated circuit (ASIC). It achieves parallelized photonic in-memory computing using phase-change-material memory arrays and photonic chip-based optical frequency combs (soliton microcombs(3)). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 gigahertz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates(3-5), ultralow-loss silicon nitride waveguides(6,7), and high-speed on-chip detectors and modulators, our approach provides a path towards full complementary metal-oxide-semiconductor (CMOS) wafer-scale integration of the photonic tensor core. Although we focus on convolutional processing, more generally our results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services. |
Databáze: | OpenAIRE |
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