Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Sensors Lab
Sensors Lab Computer, Electrical, and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia
Date
2020-06-25Online Publication Date
2020-06-25Print Publication Date
2020-08Submitted Date
2020-04-11Permanent link to this record
http://hdl.handle.net/10754/663941
Metadata
Show full item recordAbstract
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.Citation
Krestinskaya, O., Salama, K. N., & James, A. P. (2020). Automating Analogue AI Chip Design with Genetic Search. Advanced Intelligent Systems, 2000075. doi:10.1002/aisy.202000075Sponsors
Research reported in this publication was supported by the AI Initiative, King Abdullah University of Science and Technology (KAUST). The support provided through 2019 research internship program at KAUST is acknowledged.Publisher
WileyJournal
Advanced Intelligent SystemsAdditional Links
https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202000075https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aisy.202000075
ae974a485f413a2113503eed53cd6c53
10.1002/aisy.202000075
Scopus Count
Except where otherwise noted, this item's license is described as This is an open access article under the terms of the CreativeCommons Attribution License, which permits use, distribution andreproduction in any medium, provided the original work is properly cited.