Example Concept: To enhance inference speed on edge devices, you can use model quantization to reduce the precision of the model weights and activations, which decreases memory usage and computation time. Additionally, pruning can be applied to remove less important neurons or layers, reducing the model size. Optimizing the model architecture for the specific hardware, such as using depthwise separable convolutions, can also lead to faster inference. Finally, using frameworks like TensorFlow Lite or ONNX Runtime, which are designed for edge devices, can further improve performance.