# Large-Scale Optical Neural Networks based on Photoelectric Multiplication

## TL;DR

This paper introduces a scalable, high-speed, low-energy optical neural network accelerator using photoelectric multiplication, capable of reprogramming and training on the fly, with potential to surpass digital thermodynamic limits.

## Contribution

It presents a novel photonic accelerator architecture based on coherent detection for large neural networks, enabling reprogrammability, training, and ultra-low-energy operation.

## Key findings

- Achieves sub-aJ energy per MAC at GHz speeds
- Reprogrammable and trainable optical neural network design
- Potential to surpass thermodynamic limits for digital computation

## Abstract

Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic accelerator based on coherent detection that is scalable to large ($N \gtrsim 10^6$) networks and can be operated at high (GHz) speeds and very low (sub-aJ) energies per multiply-and-accumulate (MAC), using the massive spatial multiplexing enabled by standard free-space optical components. In contrast to previous approaches, both weights and inputs are optically encoded so that the network can be reprogrammed and trained on the fly. Simulations of the network using models for digit- and image-classification reveal a "standard quantum limit" for optical neural networks, set by photodetector shot noise. This bound, which can be as low as 50 zJ/MAC, suggests performance below the thermodynamic (Landauer) limit for digital irreversible computation is theoretically possible in this device. The proposed accelerator can implement both fully-connected and convolutional networks. We also present a scheme for back-propagation and training that can be performed in the same hardware. This architecture will enable a new class of ultra-low-energy processors for deep learning.