Explore the Akida event-based neural processor in this 38-minute tinyML Talks webcast featuring Kristofor Carlson from BrainChip Inc. Dive into the key distinguishing factors of Akida's computing architecture, including aggressive 1 to 4-bit weight and activation quantization, event-based implementation of machine-learning operations, and distributed computation across multiple neural processing units. Learn how these architectural innovations lead to significant reductions in MACs, parameter memory usage, and peak bandwidth requirements compared to traditional 8-bit machine learning accelerators. Discover Akida's on-chip learning capabilities using a proprietary bio-inspired algorithm, and examine its performance in few-shot learning for both visual and auditory applications. Gain insights into the chip's design and potential applications in edge computing through demonstrations and a comprehensive overview.