At Arm, we’ve leveraged three decades worth of IP design and development expertise in the mobile market to begin the process of simplifying AI and machine learning implementations for IoT. Arm’s AI platform provides flexible support for ML workloads across all programmable Arm IP, as well as partner IP, and maintains open-source software libraries, such as Arm NN, CMSIS-NN and CMSIS-DSP, to simplify development. It allows seamless integration with existing neural network frameworks, such as TensorFlow Lite for Microcontrollers, and is supported by a vibrant and diverse ecosystem, driving innovation and choice. Arm’s AI platform is the industry’s most efficient, scalable and heterogeneous in the industry and supports a development ecosystem that is redefining the capabilities of devices everywhere.
Engineers at AquaSeca, for example, are hard at work to exploit this potential. AquaSeca has developed an Arm Cortex-M4 based vibration sensor that attaches to a water pipe to form a simple and low-cost method for detecting the relative flow of water. Changes to the vibration signature might indicate cause for alarm, such as the faster flow caused by a leak, or the impeded flow caused by a blockage. The tell-tale vibrations that result from these kinds of faults are detected by the sensor and the causes inferred using several levels of ML. The AIoT system can then alert the owner before the fault escalates. Today, the AquaSeca sensor sends its data to the cloud, but engineers are migrating some of the ML inference to the sensor itself for faster responses, greater privacy, scalability and enhanced reliability.
Scaling endpoint AI for billions of devices
The latest additions to Arm’s AI portfolio include a CPU with enhanced AI capabilities, the Arm Cortex-M55 processor with the supporting Corstone-300 reference design for faster SoC implementation. It also includes the industry’s first ‘microNPU’ (neural processing unit), the Ethos-U55, which is fully integrated with the Cortex-M toolchain. These technologies enhance on-device machine learning (ML) capabilities and simplify software development for IoT and embedded applications, especially power- and size-constrained devices, which unlocks the potential of endpoint AI for all developers.