Speech recognition technology combined with artificial intelligence represents a quantum leap more accurate than past pattern recognition methods. And server-based system support for scalability, virtualization and huge amounts of unlimited storage resources that greatly contributed to the improvement of the accuracy of its prediction. However, the implementation of server-oriented reforms led to enormous latency and connectivity problems. Therefore, we propose a novel client-edge speech recognition system to enhance latency by using what we call semi-offloading technology. This proposal is promising big performance gains by offloading computing power-dependent tasks to edge nodes and processing throughput-dependent tasks by a client. The merit of semi-offloading as well as a division of workload allows for parallelism and re-ordering among the process. The experimental results show that, 23%∼62% improvement in response time.
|Title of host publication||Proceedings of the International Conference on Compliers, Architectures and Synthesis for Embedded Systems Companion, CASES 2019|
|Publisher||Association for Computing Machinery, Inc|
|Publication status||Published - 2019 Oct 13|
|Event||2019 International Conference on Compliers, Architectures and Synthesis for Embedded Systems, CASES 2019 - New York, United States|
Duration: 2019 Oct 13 → 2019 Oct 18
|Name||Proceedings of the International Conference on Compliers, Architectures and Synthesis for Embedded Systems Companion, CASES 2019|
|Conference||2019 International Conference on Compliers, Architectures and Synthesis for Embedded Systems, CASES 2019|
|Period||19/10/13 → 19/10/18|
Bibliographical noteFunding Information:
This work was supported in part by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019−0−00421, AI Graduate School Support Program), and in part by the Basic Science Research Program through the National Research Foundation of Korea by the Ministry of Education under Grant NRF−2018R1D1A1B07049842.
© 2019 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Control and Optimization