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Machine Learning on Arm Cortex-M Microcontrollers

January 20, 2022

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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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