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Although Convolutional Neural Networks (CNNs) have demonstrated the state-of-the-art inference accuracy in various intelligent applications, each CNN inference involves millions of expensive floating point multiply-accumulate (MAC) operations. To energy-efficiently process CNN inferences, prior work proposes an electro-optical accelerator to process power-of-2 quantized CNNs by electro-optical ripple-carry adders and optical binary shifters. The electro-optical accelerator also uses SRAM registers to store intermediate data. However, electro-optical ripple-carry adders and SRAMs seriously limit the operating frequency and inference throughput of the electro-optical accelerator, due to the long critical path of the adder and the long access latency of SRAMs. In this paper, we propose a photonic nonvolatile memory (NVM)-based accelerator, Light-Bulb, to process binarized CNNs by high frequency photonic XNOR gates and popcount units. LightBulb also adopts photonic racetrack memory to serve as input/output registers to achieve high operating frequency. Compared to prior electro-optical accelerators, on average, LightBulb improves the CNN inference throughput by 17× ~ 173× and the inference throughput per Watt by 17.5 × ~ 660×.
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Variational Inference Using High Speed Photonic Neural Networks
We propose a method of performing variational inference using high speed photonic neural accelerators. This method incurs no slowdown compared to deterministic photonic inference, affecting only the power consumption of existing accelerator architectures.
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- Award ID(s):
- 2128608
- PAR ID:
- 10629660
- Publisher / Repository:
- Optica Publishing Group
- Date Published:
- ISBN:
- 978-1-957171-95-1
- Page Range / eLocation ID:
- FD1.4
- Format(s):
- Medium: X
- Location:
- Denver, Colorado
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1364/FIO.2024.FD1.4
