The field of deep learning is moving towards efficient deployment of models on edge devices, with a focus on reducing latency and memory usage without sacrificing accuracy. Researchers are exploring various techniques, including post-training quantization, model pruning, and knowledge distillation, to achieve this goal. Notably, innovative approaches such as structure-aware quantization and truncation-ready training schemes are being proposed to improve the efficiency of hybrid models and vision transformers.

These advancements have significant implications for real-time applications, such as recommendation systems and computer vision tasks, where low latency and high throughput are crucial. Moreover, the development of practical and efficient quantization methods is enabling the deployment of large-scale models on edge devices, which is a critical step towards widespread adoption.

Some noteworthy papers in this area include:

• EfficientQuant, which achieves significant latency reduction with minimal accuracy loss on the ImageNet-1K dataset.
• GPLQ, a novel framework for efficient and effective vision transformer quantization, which is 100x faster than existing QAT methods and achieves highly competitive performance with FP32 models.
• TruncQuant, a truncation-ready training scheme allowing flexible bit precision through bit-shifting in runtime, demonstrating strong robustness across bit-width settings.