{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cmsis-nn-efficient-neural-network-kernels-for","title":"CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs","arxiv_id":"1801.06601","date":"2018-01-19","proceeding":null,"authors":["Liangzhen Lai","Naveen Suda","Vikas Chandra"],"abstract":"Deep Neural Networks are becoming increasingly popular in always-on IoT edge\ndevices performing data analytics right at the source, reducing latency as well\nas energy consumption for data communication. This paper presents CMSIS-NN,\nefficient kernels developed to maximize the performance and minimize the memory\nfootprint of neural network (NN) applications on Arm Cortex-M processors\ntargeted for intelligent IoT edge devices. Neural network inference based on\nCMSIS-NN kernels achieves 4.6X improvement in runtime/throughput and 4.9X\nimprovement in energy efficiency.","url_abs":"http://arxiv.org/abs/1801.06601v1","url_pdf":"http://arxiv.org/pdf/1801.06601v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cmsis-nn-efficient-neural-network-kernels-for","repo_url":"https://github.com/ARM-software/CMSIS_5","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"efficient-neural-network","task_name":"Efficient Neural Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.06601","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}