Efficient Neural Network Inference

The field studies how to run trained neural networks with less latency, memory, and energy, from phones and microcontrollers to GPUs, FPGAs, and large serving clusters. Common approaches include quantization, pruning, distillation, early exits and conditional computation, alongside compiler and kernel optimization, memory-aware scheduling, and specialized or neuromorphic hardware. Work spans vision, language, graphs, and streaming tasks, with evaluation increasingly measuring end-to-end latency, throughput, and energy on target devices rather than relying only on parameter counts or FLOPs. A central tension is that theoretical savings such as sparsity or fewer operations do not always yield faster real deployments, and efficiency gains must be balanced against accuracy, reliability, and hardware constraints.