16 research outputs found
Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment
There has been growing interest in using photonic processors for performing
neural network inference operations; however, these networks are currently
trained using standard digital electronics. Here, we propose on-chip training
of neural networks enabled by a CMOS-compatible silicon photonic architecture
to harness the potential for massively parallel, efficient, and fast data
operations. Our scheme employs the direct feedback alignment training
algorithm, which trains neural networks using error feedback rather than error
backpropagation, and can operate at speeds of trillions of multiply-accumulate
(MAC) operations per second while consuming less than one picojoule per MAC
operation. The photonic architecture exploits parallelized matrix-vector
multiplications using arrays of microring resonators for processing
multi-channel analog signals along single waveguide buses to calculate the
gradient vector for each neural network layer in situ. We also experimentally
demonstrate training deep neural networks with the MNIST dataset using on-chip
MAC operation results. Our novel approach for efficient, ultra-fast neural
network training showcases photonics as a promising platform for executing AI
applications.Comment: 15 pages, 6 figure
Silicon photonics for artificial intelligence applications
Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference