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Article

The Potential of SoC FPAAs for Emerging Ultra-Low-Power Machine Learning

Electrical and Computer Engineering (ECE), Georgia Institute of Technology, Atlanta, GA 30332, USA
Academic Editor: Andrea Acquaviva
J. Low Power Electron. Appl. 2022, 12(2), 33; https://doi.org/10.3390/jlpea12020033
Received: 31 December 2021 / Revised: 28 January 2022 / Accepted: 11 March 2022 / Published: 6 June 2022
(This article belongs to the Special Issue Low Power AI)
Large-scale field-programmable analog arrays (FPAA) have the potential to handle machine inference and learning applications with significantly low energy requirements, potentially alleviating the high cost of these processes today, even in cloud-based systems. FPAA devices enable embedded machine learning, one form of physical mixed-signal computing, enabling machine learning and inference on low-power embedded platforms, particularly edge platforms. This discussion reviews the current capabilities of large-scale field-programmable analog arrays (FPAA), as well as considering the future potential of these SoC FPAA devices, including questions that enable ubiquitous use of FPAA devices similar to FPGA devices. Today’s FPAA devices include integrated analog and digital fabric, as well as specialized processors and infrastructure, becoming a platform of mixed-signal development and analog-enabled computing. We address and show that next-generation FPAAs can handle the required load of 10,000–10,000,000,000 PMAC, required for present and future large fielded applications, at orders of magnitude of lower energy levels than those expected by current technology, motivating the need to develop these new generations of FPAA devices. View Full-Text
Keywords: machine learning; FPAA; analog computing machine learning; FPAA; analog computing
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MDPI and ACS Style

Hasler, J. The Potential of SoC FPAAs for Emerging Ultra-Low-Power Machine Learning. J. Low Power Electron. Appl. 2022, 12, 33. https://doi.org/10.3390/jlpea12020033

AMA Style

Hasler J. The Potential of SoC FPAAs for Emerging Ultra-Low-Power Machine Learning. Journal of Low Power Electronics and Applications. 2022; 12(2):33. https://doi.org/10.3390/jlpea12020033

Chicago/Turabian Style

Hasler, Jennifer. 2022. "The Potential of SoC FPAAs for Emerging Ultra-Low-Power Machine Learning" Journal of Low Power Electronics and Applications 12, no. 2: 33. https://doi.org/10.3390/jlpea12020033

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