Noise Analysis of Photonic Modulator Neurons

07/17/2019
by   Thomas Ferreira de Lima, et al.
0

Neuromorphic photonics relies on efficiently emulating analog neural networks at high speeds. Prior work showed that transducing signals from the optical to the electrical domain and back with transimpedance gain was an efficient approach to implementing analog photonic neurons and scalable networks. Here, we examine modulator-based photonic neuron circuits with passive and active transimpedance gains, with special attention to the sources of noise propagation. We find that a modulator nonlinear transfer function can suppress noise, which is necessary to avoid noise propagation in hardware neural networks. In addition, while efficient modulators can reduce power for an individual neuron, signal-to-noise ratios must be traded off with power consumption at a system level. Active transimpedance amplifiers may help relax this tradeoff for conventional p-n junction silicon photonic modulators, but a passive transimpedance circuit is sufficient when very efficient modulators (i.e. low C and low V-pi) are employed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/04/2022

Energy-Efficient High-Accuracy Spiking Neural Network Inference Using Time-Domain Neurons

Due to the limitations of realizing artificial neural networks on preval...
research
03/12/2021

The general aspects of noise in analogue hardware deep neural networks

Deep neural networks unlocked a vast range of new applications by solvin...
research
11/26/2018

Noisy Computations during Inference: Harmful or Helpful?

We study two aspects of noisy computations during inference. The first a...
research
11/02/2015

Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers

Solving constraint satisfaction problems (CSPs) is a notoriously expensi...
research
07/12/2021

An active dendritic tree can mitigate fan-in limitations in superconducting neurons

Superconducting electronic circuits have much to offer with regard to ne...
research
08/25/2022

CMOS-based area-and-power-efficient neuron and synapse circuits for time-domain analog spiking neural networks

Conventional neural structures tend to communicate through analog quanti...
research
04/20/2022

Noise mitigation strategies in physical feedforward neural networks

Physical neural networks are promising candidates for next generation ar...

Please sign up or login with your details

Forgot password? Click here to reset