Presentation
P-ReTi: Photonic Tensor Core for Real-Time Learning
DescriptionSeveral novel AI accelerators based on ASIC, FPGA, and resistive-memory devices have been recently demonstrated with promising results. Most of them target only the inference (testing) phase of deep learning. There have been very limited attempts to design a full-fledged AI accelerator capable of both training and inference in real-time. It is due to the highly compute and memory intensive nature of the training phase. In this paper, we propose P-ReTi, a novel analog photonics CNN accelerator which uses silicon microdisk-based convolution, photonic phase change memory-based memory, and dense-wavelength-division-multiplexing for energy-efficient and ultrafast deep learning. Compared to the state-of-the-art, P-ReTi improves the CNN throughput, energy-efficiency, and computational efficiency by up to 48×, 45×, and 12× respectively with trivial accuracy degradation.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security