Automating Analogue AI Chip Design with Genetic Search. (25th June 2020)

• Record Type: Journal Article
• Authors: Krestinskaya, Olga; Salama, Khaled N.; James, Alex P.
• Is Part Of: Advanced intelligent systems. Volume 2:Number 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-25
• Subjects: genetic algorithm -- hyperparameter selection -- memristive networks -- memristor nonidealities -- neuromorphic chip design; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202000075
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : 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. Abstract : An automated computer‐aided design solution with genetic search for designing analogue neuromemristive chips is used. The approach aims to lower design cost, ease of scaling, high‐energy efficiency, and small on‐chip area.