KAUST DepartmentSensors Lab Computer, Electrical, and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia
Electrical Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/663941
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AbstractOptimization 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.
CitationKrestinskaya, O., Salama, K. N., & James, A. P. (2020). Automating Analogue AI Chip Design with Genetic Search. Advanced Intelligent Systems, 2000075. doi:10.1002/aisy.202000075
SponsorsResearch 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.
JournalAdvanced Intelligent Systems
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