Poster Abstract: Unifying On-device Tensor Program Optimization through Large Foundation Model
Zhihe Zhao, Neiwen Ling, Kaiwei Liu, Nan Guan, Guoliang Xing · 2023
We present TensorBind, a novel approach aimed at unifying different hardware architectures for compilation optimization. Our proposed framework establishes an embedding space to seamlessly bind diverse hardware platforms together. By leveraging this unified representation, TensorBind enables efficient tensor program optimization techniques across a wide range of hardware platforms. We provide experimental results demonstrating the essentiality and adaptability of TensorBind in translating tensor program optimization records across multiple hardware architectures, thus revolutionizing compilation optimization strategies and facilitating the development of high-performance compilation systems over heterogeneous devices.