2024-12-02 マサチューセッツ工科大学(MIT)
＜関連情報＞
- https://news.mit.edu/2024/photonic-processor-could-enable-ultrafast-ai-computations-1202
- https://www.nature.com/articles/s41566-024-01567-z
順方向のみの学習を行うシングルチップのフォトニック・ディープ・ニューラル・ネットワーク Single-chip photonic deep neural network with forward-only training
Saumil Bandyopadhyay,Alexander Sludds,Stefan Krastanov,Ryan Hamerly,Nicholas Harris,Darius Bunandar,Matthew Streshinsky,Michael Hochberg & Dirk Englund
Nature Photonics Published:02 December 2024
DOI:https://doi.org/10.1038/s41566-024-01567-z
Abstract
As deep neural networks revolutionize machine learning, energy consumption and throughput are emerging as fundamental limitations of complementary metal–oxide–semiconductor (CMOS) electronics. This has motivated a search for new hardware architectures optimized for artificial intelligence, such as electronic systolic arrays, memristor crossbar arrays and optical accelerators. Optical systems can perform linear matrix operations at an exceptionally high rate and efficiency, motivating recent demonstrations of low-latency matrix accelerators and optoelectronic image classifiers. However, demonstrating coherent, ultralow-latency optical processing of deep neural networks has remained an outstanding challenge. Here we realize such a system in a scalable photonic integrated circuit that monolithically integrates multiple coherent optical processor units for matrix algebra and nonlinear activation functions into a single chip. We experimentally demonstrate this fully integrated coherent optical neural network architecture for a deep neural network with six neurons and three layers that optically computes both linear and nonlinear functions with a latency of 410 ps, unlocking new applications that require ultrafast, direct processing of optical signals. We implement backpropagation-free in situ training on this system, achieving 92.5% accuracy on a six-class vowel classification task, which is comparable to the accuracy obtained on a digital computer. This work lends experimental evidence to theoretical proposals for in situ training, enabling orders of magnitude improvements in the throughput of training data. Moreover, the fully integrated coherent optical neural network opens the path to inference at nanosecond latency and femtojoule per operation energy efficiency.
