SensiML Corporation, a leading developer of AI tools for building intelligent IoT endpoints, announced that its SensiML Analytics Toolkit now supports AI-based sensor algorithms running on the STEVAL-MKSBOX1V1 SensorTile.Box IoT kit from STMicroelectronics. This announcement builds on SensiML’s previous support of SensorTileV1 announcement in early January that the company had joined the STMicroelectronics Partner Program, enabling the two organizations to work together towards delivering complete AI-based solutions for IoT endpoint applications.
Since the SensiML Analytics Toolkit features an end-to-end AI workflow, developers using STM32 microcontrollers and motion MEMS sensors now have an AI development tool that supports data collection, labeling, feature extraction, ML classification and auto code generation. Automation built into the tool drastically reduces development time and cost, allowing projects ranging from single users to large teams to generate optimized edge AI sensor algorithms in a fraction of the time that would have otherwise been required with hand-coding.
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“With the broad collection of eight valuable MEMS sensors, a Bluetooth Smart radio, and an ultra-low-power STM32-based control embedded within SensorTile.Box, pairing this wireless IoT and wearable ready-to-use box kit with SensiML makes generating useful and powerful edge AI models easier than ever,” said Simone Ferri, MEMS Sensors Division Director, STMicroelectronics.
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“The SensiML Analytics Toolkit is the only end-to-end AI toolkit designed by and for teams requiring production-grade workflows and a worldwide support presence,” said Chris Rogers, CEO at SensiML. “With rich data collection and labeling features, model building that includes both point-and-click GUI support as well as Python language extensions for expert users, developers using STM32 MCUs and motion MEMS sensors can be sure their teams won’t outgrow their edge AI tool or face project delays resulting from flawed initial training data.”
Together the ST kit along with the SensiML tools enable up to five times faster development of even the most complex AI-based pattern recognition algorithms for time-series sensors operating at the extreme edge of the network than could be done using more traditional methods.
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