TinyML & Edge AI: Machine Learning on Devices

In this episode, we explore how AI is moving from the cloud to tiny devices. TinyML is the field of optimizing models and algorithms to run on microcontrollers, smartphones, and other edge devices with very limited compute and power. We discuss techniques like model compression, quantization, and architecture search that make models small and efficient enough to fit on a $5 microcontroller, bringing capabilities like wake-word detection, sensor analytics, or even vision tasks directly onto devices. You’ll hear about examples like MCUNet, an MIT system that achieved ImageNet-level vision recognition on a microcontroller, and why on-device AI can be beneficial (low latency, no internet needed, data privacy). We also cover real-world applications already using TinyML, from smart appliances to wearable health monitors.