With the rapid development of AI and deep learning, computational demands are increasing significantly. While GPUs excel in parallel computing, they fall short in terms of energy efficiency, specialization, and processing latency. In contrast, Neural Processing Units (NPUs), such as the Ascend NPUs, designed specifically for deep learning tasks, demonstrate superior performance. However, the architecture specialization makes operator development more challenging, leading to a reliance on manual tuning and optimization, which incurs significant time cost and developing effort. To address this issue, we propose NPUMeter, an automatic operator optimization framework for Ascend NPUs built upon accurate and comprehensive analytical performance models. NPUMeter comprises two components: (1) an analytical performance model that accurately estimates operator latency on NPU given different configurations of optimization parameters; (2) an efficient design space exploration (DSE) algorithm that automatically searches for the optimal parameter configuration in a large design space within minutes. Experimental results demonstrate that NPUMeter achieves high estimation accuracy, with an average error below 5%. It effectively generates near-optimal configurations for various operators, achieving up to a 1.46× performance speedup compared to the configuration generated by the Ascend C compiler while reducing the DSE time from hours to minutes.

The main theme of this paper is to develop a systematic framework to learn a Mahalanobis distance metric based on matrix sketching. Within this framework, we present a novel sketch metric learning algorithm which sequentially sketches the received samples from training dataset and formulates a new kind of constraint for metric learning. This is in contrast to the traditional constraints that are only consisted of data from training dataset. In this paper, one training instance in the constraint is replaced by a pseudo center, which is generated during the sketching stage. Due to this change, our learning algorithm can focus on pushing every received sample to its corresponding similar pseudo center closer and pulling it far away from the dissimilar one. In addition, it can further achieve better performance of some kinds of time-varying process (e.g. on object tracking) than the compared related competitors. We demonstrate how to implement other methods in our algorithm framework and experiment to show that our method outperforms the competitors and relevant baselines on multiple datasets.