Watch the video of Chris Rogers’ conversation with Ross Sabolcik, VP/GM of Industrial and Commercial IoT products at Silicon Labs, about the benefits of AI at the edge, key use cases, and how Silicon Labs and SensiML are working together to advance IoT smart sensing with ML.
Opportunities to improve manufacturing processes by adding machine learning sensors at the IoT edge are rapidly emerging. Thanks to a combination of Powerful microcontrollers and multi-core SoCs like the EOS S3 on QuickLogic’s QuickAI HDK High-resolution, low-cost MEMS sensors and microphones Powerful AutoML-based AI tools like SensiML Analytics Toolkit it is possible to bring sophisticated
Today we announced that our SensiML Analytics Toolkit supports NXP’s i.MXT RT portfolio of crossover microcontrollers and their associated i.MX RT1050 Evaluation Kit. This announcement is significant as it gives users of those devices a complete AI-based sensor algorithm development solution for IoT endpoints. With this, NXP customers using the i.MX RT crossover MCUs can
Artificial Intelligence (AI) is becoming increasingly commonplace as organizations seek to bring effective decision making and operational efficiencies to business in ways that transform how humans and machines work together. As this transformation takes shape, the advantages of edge-based AI implementations over centralized or cloud-based models is changing how AI tools are being deployed and
The Smart Manufacturing Innovation Center (SMIC) showcased SensiML and QuickLogic Quick AI industrial sensing this past month as part of their pilot manufacturing demonstration. The demonstration and multi-phase pilot for rapid configuration modular manufacturing cells with predictive maintenance are functioning at their R&D center based in Seoul, South Korea. The pilot demonstrates how flexible manufacturing
