Machine Learning

Machine Learning on Arm Cortex-M Microcontrollers

January 20, 2022

Machine_Learning_on
Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing neural network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. To enable that, we present CMSIS-NN, an open-source library of optimized software kernels that maximize the NN performance on Cortex-M cores with minimal memory footprint overhead. We further present methods for NN architecture exploration, using image classification on CIFAR-10 dataset as an example, to develop models that fit on such constrained devices.

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Interfacing Technologies Corproation provide process-driven quality, compliance and low code digitalization continuous improvement solutions (compliant with ISO 27001, ITIL, SOX, 21CFR part 11, GxP, ISO 9001, GDPR) In addition to numerous awards, the Interfacing Enterprise Process Center (EPC) has been recognized by Gartner as a leading digital management software for multiple years now within 4 independent market categories, including: Enterprise Business Process Analysis (eBPA), Business Operating System (BOS), Digital Twin (DTO) & Operational Intelligence (OI).

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