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10 March 2021 Scalability and noise in (photonic) hardware neural networks
Nadezhda Semenova, Johnny Moughames, Xavier Porte, Muamer Kadic, Laurent Larger, Daniel Brunner
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Proceedings Volume 11703, AI and Optical Data Sciences II; 117030I (2021) https://doi.org/10.1117/12.2585745
Event: SPIE OPTO, 2021, Online Only
Abstract
Analog neural networks are promising candidates for overcoming the sever energy challenges of digital Neural Network processors. However, noise is an inherent part of analogue circuitry independent if electronic, optical or electro-optical integration is the target. I will discuss fundamental aspects of noise in analogue circuits and will then introduce our analytical framwork describing noise propagation in fully trained deep neural networks comprising nonlinear neurons. Most importantly, we found that noise accumulation can be very efficiently supressed under realistic hardware conditions. As such, neural networks implemented in analog hardware should be very robust to internal noise, which is of fundamental importance for future hardware realizations.
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Nadezhda Semenova, Johnny Moughames, Xavier Porte, Muamer Kadic, Laurent Larger, and Daniel Brunner "Scalability and noise in (photonic) hardware neural networks", Proc. SPIE 11703, AI and Optical Data Sciences II, 117030I (10 March 2021); https://doi.org/10.1117/12.2585745
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KEYWORDS
Neural networks
Lithography
Wave propagation
Energy efficiency
Neurons
Photonics systems
Statistical analysis
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