SensiML offers an IoT AI software program that enables developers to build intelligent endpoints quickly and easily (up to five times faster than hand-coded solutions). Now with the availability of the free trial and Data Depot dataset repository, developers have the opportunity to easily preview the toolkit and learn how SensiML can vastly accelerate IoT algorithm development prior to purchasing.
The SensiML Analytics Toolkit delivers significant benefits for both the AI endpoint product, as well as the development process for that product. Data science is built into the toolkit, making it usable for mainstream developers – there is no need for a team of AI experts. The toolkit generates code that runs on MCUs (rather than CPUs), enabling the delivery of practical AI on embedded wireless IoT devices. It also increases power efficiency, with built-in automatically generated optimizations for MCUs, DSPs and FPGAs. All phases of design development are supported, including raw signal capture, data insight and labeling, algorithm generation, firmware code generation, and test validation and support. By automating the implementation of AI-based designs, the toolkit reduces the time and resources typically needed, from five engineer years to just four engineer months.
Applications for the SensiML Analytics Toolkit include a broad array of time-series sensor intelligence usages across consumer and industrial IoT devices. Examples of such applications include predictive maintenance, safety wearables, smart appliances, fitness wearables and industrial monitoring, to name just a few. Sample application datasets and documentation are now available for exploration and use in the SensiML Data Depot dataset repository (https://datadepot.https://sensiml.com), which provides a curated set of labeled datasets for a growing list of applications.
The toolkit itself is made up of three components: the SensiML Data Capture Lab, Analytics Studio, and the TestApp. Together, these components provide a complete end-to-end development solution. Multiple hardware evaluation platforms are supported as well, including the QuickLogic Merced and Chilkat platforms, Raspberry Pi, the ST Sensor Tile and Nordic Thingy to speed evaluation and subsequent design development.
The free trial version of the SensiML Analytics Toolkit includes:
– Data collection and labeling (using sample datasets)
– Automated ML algorithm creation
– Algorithm performance visualization
– Auto-generation of optimized device code
– Embedded binary executable output (limited number of classification results)
– Device test/validation application
