Fasor: A Fast Tensor Program Optimization Framework for Efficient DNN Deployment
Year: 2024
Type: conference-paper
Abstract: With the growing importance of deploying deep neural networks (DNNs), there are increasing demands to improve both the efficiency and quality of tensor program optimization (TPO). TPO involves searching for possible program transformations for a given tensor program on target hardware to optimize its execution. TPO is challenging and expensive due to the exponential combinations of transformations and time-consuming on-device measurement of transformations. While prior research has primarily focused on the quality of TPO, i.e., generating high-performance tensor programs, there has been less emphasis on the efficiency of TPO, i.e., optimizing tensor programs with low optimization time overhead.
Authors: Hanxian Huang, Xin Chen, Jishen Zhao
Institutions: University of California San Diego, Intel (United States)
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4399282189