IEICE Transactions on Electronics
Online ISSN : 1745-1353
Print ISSN : 0916-8524

This article has now been updated. Please use the final version.

Optoelectronic Pipeline Architecture of Convolutional RNN for Energy Efficient Inference at the Speed of Light
Chunlu WANGYutaka MASUDATohru ISHIHARA
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JOURNAL FREE ACCESS Advance online publication

Article ID: 2024LHP0004

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Abstract

This paper proposes an optoelectronic architecture of Convolutional Recurrent Neural Network (C-RNN in short). It employs RNN layers that replace area-consuming fully connected (FC) layers in typical convolutional neural network (CNN) architectures. The convolution and RNN layers in this architecture process input data in a pipelined manner that improves the throughput of the inference processing. It takes advantage of both the high input compression capabilities of CNNs and the compact and power-efficient nature of RNNs. The proposed optoelectronic C-RNN architecture achieves over 97.8% accuracy on the MNIST dataset while maintaining the advantages of power-efficient and high-speed characteristics of photonics. Our proposed optoelectronic C-RNN architecture can reach 240 TOPs/W, which is ten times more efficient than CMOS-based dedicated CNN accelerators.

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