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Limited memory and computing methods: AI learning models should have a minimum footprint. So they are often operate on microcontrollers that have constrained memory and computing sources.
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The data module handles and procedures data from sensors and various resources. The algorithm module integrates quite a few AI algorithms and manages many model files. The inference module processes real-time data with the AI algorithm to put into practice AI capabilities dependant on the outcome.
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Faster response times: TinyML models may make predictions or decisions on devices without the necessity to mail data towards the cloud, which may end up in faster response times for users.
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