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Title: MERIT: A Sustainable DNN Accelerator Design with Photonic Phase-Change Memory
The growing computational demands of deep learning have driven interest in analog neural networks using resistive memory and silicon photonics. However, these technologies face inherent limitations in computing parallelism when used independently. Photonic phase-change memory (PCM), which integrates photonics with PCM, overcomes these constraints by enabling simultaneous processing of multiple inputs encoded on different wavelengths, significantly enhancing parallel computation for deep neural network (DNN) inference and training. This paper presents MERIT, a sustainable DNN accelerator that capitalizes on the non-volatility of resistive memory and the high operating speed of photonic devices. MERIT enables seamless inference and training by loading weight kernels into photonic PCM arrays and selectively supplying light encoded with input features for the forward pass and loss gradients for the backward pass. We compare MERIT with state-of-the-art digital and analog DNN accelerators including TPU, DEAP, and PTC. Simulation results demonstrate that MERIT reduces execution time by 68% and energy consumption by 64% for inference, and reduces execution time by 79% and energy consumption by 84% for training.  more » « less
Award ID(s):
1936794 2311543 2321225 2324645 2311544 1901165 2321224 2324644
PAR ID:
10609586
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Transactions on Sustainable Computing
ISSN:
2377-3790
Page Range / eLocation ID:
1 to 12
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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