Explore the challenges and opportunities of AI/ML solutions for low-power Edge platforms in this 55-minute tinyML Talks webcast. Dive into the complexities of implementing AI/ML applications on various Edge devices, from microcontroller-based systems to application processors and servers. Learn about the diverse compute types, operating systems, and acceleration libraries available for Edge computing. Discover how GMAC Intelligence is developing an on-device AI/ML library and API to simplify application development and enable on-device training. Gain insights into topics such as AlwaysOn AI, video attendance, face recognition, and leveraging tinyML. Explore hardware acceleration techniques, practical problems in low-power devices, and unique algorithms for Edge AI. Discuss the future of Edge AI and learn about emerging platforms like Deeplight, Edge Impulse, Kixo, and Reality AI.