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CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs

arXiv 1801.06601 ↗First posted 19 Jan 2018cs.NE cs.LG cs.MScited by 121

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Abstract (arXiv)

Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices performing data analytics right at the source, reducing latency as well as energy consumption for data communication. This paper presents CMSIS-NN, efficient kernels developed to maximize the performance and minimize the memory footprint of neural network (NN) applications on Arm Cortex-M processors targeted for intelligent IoT edge devices. Neural network inference based on CMSIS-NN kernels achieves 4.6X improvement in runtime/throughput and 4.9X improvement in energy efficiency.

Key facts. Cited by 121 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jan 2018.

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Citing papers per year: 2021: 20; 2022: 18; 2023: 26; 2024: 16; 2025: 14; 2026: 27.

121 papers.

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  30. 2022TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inferencecited by 4
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