Development of an Open ISA GPGPU for Edge Device Machine Learning Applications

Yu Xiang Su, Jhi Han Jheng, Dun Jie Chen, Chung Ho Chen

研究成果: Conference contribution

摘要

Hosting the deep learning model on the cloud may not be the best solution in many cases, for instance, IoT applications or autonomous system where low latency or enhanced security is desirable. Deep learning on the edge alleviates the above issues, and provides benefits of local computation. In this paper, we present the development of an open ISA (instruction set architecture) general purpose GPU aimed at edge computation. Our GPU, CASLab GPU, uses license-free, royalty-free HSAIL ISA specification and supports OpenCL1.2/2.0 APIs for heterogeneous computing. CASLab GPU also supports TensorFlow framework with CUDA-on-CL technology. CASLab GPU IP with configurable SIMT Core design tailors directly to the computing need of on-device learning and inference. The GPU is developed in ESL design methodology which incorporates GPU micro-architecture exploration, power modelling of the GPU, and the co-simulation of the GPU software stack.

原文English
主出版物標題ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks
發行者IEEE Computer Society
頁面214-217
頁數4
ISBN(電子)9781728113395
DOIs
出版狀態Published - 2019 七月
事件11th International Conference on Ubiquitous and Future Networks, ICUFN 2019 - Zagreb, Croatia
持續時間: 2019 七月 22019 七月 5

出版系列

名字International Conference on Ubiquitous and Future Networks, ICUFN
2019-July
ISSN(列印)2165-8528
ISSN(電子)2165-8536

Conference

Conference11th International Conference on Ubiquitous and Future Networks, ICUFN 2019
國家/地區Croatia
城市Zagreb
期間19-07-0219-07-05

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 電腦科學應用
  • 硬體和架構

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