On retargeting the AI programming framework to new hardwares

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Abstract

Nowadays, a large number of accelerators are proposed to increase the performance of AI applications, making it a big challenge to enhance existing AI programming frameworks to support these new accelerators. In this paper, we select TensorFlow to demonstrate how to port the AI programming framework to new hardwares, i.e., FPGA and Sunway TaihuLight here. FPGA and Sunway TaihuLight represent two distinct and significant hardware architectures for considering the retargeting process. We introduce our retargeting processes and experiences for these two platforms, from the source codes to the compilation processes. We compare the two retargeting approaches and demonstrate some preliminary experimental results.

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APA

Zhao, J., Chang, Y., Li, D., Xia, C., Cui, H., Zhang, K., & Feng, X. (2018). On retargeting the AI programming framework to new hardwares. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11276 LNCS, pp. 39–51). Springer Verlag. https://doi.org/10.1007/978-3-030-05677-3_4

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