Authors: Alexander Song, Sai Nikhilesh Murty Kottapalli, Rahul Goyal, Bernhard Schölkopf, Peer Fischer

Published on: February 03, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.01988

Summary

• What is new: A multilayer optoelectronic computing framework that combines optical and optoelectronic layers for real-time, parallelized operations.
• Why this is important: Existing optical approaches to computing are slowed down by inefficient read-in and read-out data processes.
• What the research proposes: The introduced framework alternates between optical layers for matrix-vector multiplications and optoelectronic layers for rectified linear functions, minimizing read-in and read-out requirements.
• Results: Achieved a 92% recognition accuracy on the MNIST database and 86% classification accuracy on nonlinear spiral data.

Technical Details

Technological frameworks used: Multilayer optoelectronic

Models used: 3-layer network with two hidden layers

Data used: MNIST database, nonlinear spiral data

Potential Impact

Existing computing hardware companies, AI technology providers, and companies in need of high-speed, energy-efficient computing solutions for deep learning applications.

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