MicroAI, the pioneer in edge-native artificial intelligence (AI) and machine learning (ML) products, announced today that it has joined Silicon Labs’ (NASDAQ: SLAB) Technology Partner Program and begun to collaborate to deliver the benefits of edge-native AI for Silicon Labs’ customers.
Silicon Labs is a leader in secure, intelligent wireless technology for a more connected world. MicroAI is the pioneer in edge-native AI technology that personalizes AI on connected endpoints by enabling training and inferencing on each unique edge-connected device. This collaboration between the companies will enable Silicon Labs customers an accelerated path to designing, developing, and deploying next-generation connected devices that personalize AI for each unique end-user, through mass customization and contextualization of their devices’ unique environment.
MicroAI’s AtomML™ is a next-generation AI solution that enables an AI algorithm to run on simple devices leveraging wireless connectivity with limited memory and CPU capacity; an approach that provides adopters with greater design flexibility, lower cost, faster time to market, and quicker ROI. AtomML provides breakthrough, edge-native AI capabilities that personalizes AI on next generation edge devices with AI training and inferencing that is unique on each and every edge endpoint. With a small compute footprint and these self-learning capabilities, this solution provides sophisticated personalized and context-aware intelligence for high value use-cases that include condition monitoring, security, anomaly detection, and predictive maintenance.
Silicon Labs ‘Works With’ Virtual Conference
Yasser Khan, CEO of MicroAI, will present at the upcoming Silicon Labs Works With Conference on September 15 at 10:00am (CDT). The presentation, entitled “Benefits of Enabling Artificial Intelligence & Machine Learning on the Edge,” will provide Silicon Labs customers with an inside look on how edge-native AI solves the business and technical challenges of legacy cloud and hybrid-cloud AI edge approaches, and how OEMs can circumvent cumbersome modeling tool approaches that unnecessarily add cost, complexity and time to get next-gen devices to market.
