Lightspeed Binary Neural Networks using Optical Phase-Change Materials

Conference paper (2023)

Authors

Taha Shahroodi Computer Engineering -

Rafaela Cardoso École Centrale de Lyon

M.Z. Zahedi Computer Engineering -

J.S.S.M. Wong Computer Engineering -

Alberto Bosio École Centrale de Lyon

Ian O'Connor École Centrale de Lyon

S. Hamdioui Quantum & Computer Engineering

Research Group

Computer Engineering () (TU Delft)

DOI: https://doi.org/10.23919/DATE56975.2023.10137229

Computation-In-Memory Binary Neural Network Optical PCM

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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Computer Engineering

Abstract

This paper investigates the potential of a compute-in-memory core based on optical Phase Change Materials (oPCMs) to speed up and reduce the energy consumption of the Matrix-Matrix-Multiplication operation. The paper also proposes a new data mapping for Binary Neural Networks (BNNs) tailored for our oPCM core. The preliminary results show a significant latency improvement irrespective of the evaluated network structure and size. The improvement varies from network to network and goes up to ~1053x.

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- Embargo expired in 02-12-2023