Open AccessAutomating Analogue AI Chip Design with Genetic Search
Author(s) -
Krestinskaya Olga,
Salama Khaled N.,
James Alex P.
Publication year - 2020
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202000075
Subject(s) - computer science , electronic design automation , automation , chip , software , computer hardware , embedded system , circuit design , computer architecture , engineering , mechanical engineering , telecommunications , programming language
Optimization of analogue neural circuit designs is one of the most challenging, complicated, time‐consuming, and expensive tasks. Design automation of analogue neuromemristive chips is made difficult by the need to design chips at low cost, ease of scaling, high‐energy efficiency, and small on‐chip area. The rapid progress in edge AI computing applications generates high demand for developing smart sensors. The integration of high‐density analogue computing AI chips as coprocessing units to sensors is gaining popularity. This article proposes a hardware–software codesign framework to speed up and automate the design of analogue neuromemristive chips. This work uses genetic algorithms with objective functions that take into account hardware nonidealities such as limited precision of devices, the device‐to‐device variability, and device failures. The optimized neural architectures and hyperparameters successfully map with the library of relevant neuromemristive analogue hardware blocks. The results demonstrate the advantage of proposed automation to speed up the analogue circuit design of large‐scale neuromemristive networks and reduce overall design costs for AI chips.