CMOS Analog Artificial Neural Network

This paper focuses on the design and simulation of low power analog artificial neural network (ANN) using 180nm CMOS technology in Cadence Virtuoso. The neurons here are designed in such a manner that they could perform key biological functions, namely multiplication of the signal, summation, and thresholding. The design exploits advantages of analog computation, leading to lower power dissipation and better processing speed in comparison to purely digital implementations. This makes it particularly suitable for real-time applications. Simulations are performed at different process corners for power consumption analysis to ensure the performance of the network, establishing its efficiency for low-power AI solutions. The key design components include Gilbert cell multipliers for weight multiplication and CMOS-based sigmoid activation functions to provide efficient analog alternatives of the traditional digital elements of a neural network.

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Publication Details

Published
2025-04-05
DOI
https://doi.org/10.1109/devic63749.2025.11012358
Primary Topic
Analog and Mixed-Signal Circuit Design
Type
article
Field-Weighted Citation Impact
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article

CMOS Analog Artificial Neural Network

Suman Lata Tripathi, S Harshith, K. Ramesh Babu, Jagmeet Singh
Analog and Mixed-Signal Circuit Design
article

CMOS Analog Artificial Neural Network

Suman Lata Tripathi, S Harshith, K. Ramesh Babu, Jagmeet Singh
article en

Abstract

This paper focuses on the design and simulation of low power analog artificial neural network (ANN) using 180nm CMOS technology in Cadence Virtuoso. The neurons here are designed in such a manner that they could perform key biological functions, namely multiplication of the signal, summation, and thresholding. The design exploits advantages of analog computation, leading to lower power dissipation and better processing speed in comparison to purely digital implementations. This makes it particularly suitable for real-time applications. Simulations are performed at different process corners for power consumption analysis to ensure the performance of the network, establishing its efficiency for low-power AI solutions. The key design components include Gilbert cell multipliers for weight multiplication and CMOS-based sigmoid activation functions to provide efficient analog alternatives of the traditional digital elements of a neural network.

Lovely Professional University (IN)
Openalex Percentile: Top 89%
Analog and Mixed-Signal Circuit Design
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CMOS Analog Artificial Neural Network — Suman Lata Tripathi, S Harshith, et al. · (2025) | TGRS Research Map | TGRS