Recent advances in Deep Neural Network (DNN) optimization enable the deployment of these models on resource-constrained embedded devices to perform various tasks. Multiple techniques, such as quantization and pruning, have been successfully applied to reduce the memory, energy, and latency requirements on edge devices. While these improvements enable edge inference, training directly at the edge remains a challenging task, despite its potential benefits for robust and adaptive AI systems. At the same time, low-resolution infrared (IR) cameras have numerous applications as these unobtrusive, privacy-preserving and inexpensive sensors can capture enough information to perform various tasks. By coupling a microprocessor to such IR sensors, it is possible to enable a rich collection of applications that would benefit from DNN inference and training directly on device.