Quantization is an extremely active area in improving the efficiency of DNN inference. Quantization works by reducing the precision of weights and date to make storage and computation more efficient. The potential downside of quantization is a reduction in the performance (e.g., classification accuracy for image classification) of the quantized model.
So far, my research in this area has mainly focused on the deployment aspect of quantization: how can we efficiently use quantized models on real systems? This question has different answers based on the precision of the model (e.g., 1-bit versus 8-bit models) and the hardware the model will be deployed on (e.g., embedded devices, FPGAs, GPUs, etc…).
B. McDanel, H. T. Kung, S. Zhang
IEEE International Symposium on Circuits and Systems (ISCAS), 2021
H. T. Kung, B. McDanel, S. Zhang
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2020.
B. McDanel, S. Zhang, H. T. Kung, X. Dong.
32nd ACM International Conference on Supercomputing (ICS), 2019