Ultra-Low Power Electronic Circuits Inspired by Biological Genetic Processes

Ilan Oren, Raghd Abu-Sinni, Ramez Daniel

2023

Abstract

Neuromorphic engineering, inspired by principles and architecture of neuronal circuitries, enabled the design of Artificial Neural networks (ANNs) for Intelligent systems. These systems perform very complex computation tasks, yet they consume significant power. Thus, using artificial intelligence (AI) for applications where only a small power source is available is very limited. While the neuronal networks in the brain can recognize complex patterns and memorize enormous elements, molecular and protein networks can perform other complex tasks such as adaptive immunity and cell differentiation at high energy efficiency. Here, we claim that a bio-inspired computing platform mimicking molecular protein networks can lead to ultra-low power emergent computation. Previously, we proposed a molecular-inspired computing model named Perceptgene that has the attributes of learning and adaptivity as the neural network (Rizik et al., 2022). Similarities were found between equations describing biochemical reactions and transistor operation at subthreshold (Sarpeshkar, 2011) enabling the design of Perceptgene with subthreshold electrical circuits. Thus, the subthreshold Perceptgene circuits are expected to allow computing and learning capabilities at ultra-low power consumption.

Paper Citation

in Harvard Style

Oren I., Abu-Sinni R. and Daniel R. (2023). Ultra-Low Power Electronic Circuits Inspired by Biological Genetic Processes. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 1: BIODEVICES; ISBN 978-989-758-631-6, SciTePress, pages 150-156. DOI: 10.5220/0011707800003414

in Bibtex Style

@conference{biodevices23,
author={Ilan Oren and Raghd Abu-Sinni and Ramez Daniel},
title={Ultra-Low Power Electronic Circuits Inspired by Biological Genetic Processes},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 1: BIODEVICES},
year={2023},
pages={150-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011707800003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 1: BIODEVICES
TI - Ultra-Low Power Electronic Circuits Inspired by Biological Genetic Processes
SN - 978-989-758-631-6
AU - Oren I.
AU - Abu-Sinni R.
AU - Daniel R.
PY - 2023
SP - 150
EP - 156
DO - 10.5220/0011707800003414
PB - SciTePress