The field of neural network deployment is moving towards more efficient and adaptive methods. Researchers are exploring various techniques to reduce the computational and memory costs of large-scale deep learning, such as dynamic quantization, mixed-precision training, and stochastic computing. These methods aim to improve the performance-latency trade-off and enable the deployment of neural networks on resource-constrained devices. Notably, papers such as DP-LLM and DQT have introduced novel mechanisms for dynamic precision assignment and dequantization-free nested integer arithmetic, respectively. Another significant direction is the development of hybrid quantization algorithms, such as PTQAT, which combines post-training quantization and quantization-aware training to achieve efficient deployment of 3D perception networks. Overall, the field is advancing towards more efficient and specialized solutions for neural network deployment.

Noteworthy papers include: DP-LLM, which achieves a superior performance-latency trade-off through dynamic precision assignment. DQT, which enables dequantization-free dynamic quantization through a novel nested integer representation. PTQAT, which combines post-training quantization and quantization-aware training for efficient deployment of 3D perception networks.