Explore full-stack optimization techniques for diverse edge AI platforms in this tinyML Summit 2022 conference talk. Delve into the challenges of deploying TinyML on resource-constrained devices and discover innovative solutions to improve neural network efficiency. Learn about model compression, neural architecture rebalancing, and new design primitives that address the mismatch between AI models and hardware. Gain insights into deploying real-world AI applications on tiny microcontroller units (MCUs) despite limited memory and compute power. Examine topics such as patch-based inference, MCUNet-v2, Once-for-All Network, NetAug for TinyML, TinyTL, and full-stack LIDAR and point cloud processing. Understand the fundamental problems in TinyML and explore how OmniML's approach of compressing models before training enables TinyML for various vision tasks.