AI in Embedded Systems

AI on embedded systems prioritizes low latency and energy by using quantization, pruning, and lightweight architectures. Workflows involve quantization-aware training or post-training quantization, hardware-aware optimizations (TVM, TF Lite), and benchmarking on target devices.