Work-in-progress: Computation offloading of acoustic model for client-edge-based speech-recognition
- Authors
- Lee Y.-M.[Lee Y.-M.]; Yang J.-S.[Yang J.-S.]
- Issue Date
- 2019
- Publisher
- Association for Computing Machinery, Inc
- Keywords
- Edge Computing; Semi-Offloading; Speech-Recognition
- Citation
- Proceedings of the International Conference on Compliers, Architectures and Synthesis for Embedded Systems Companion, CASES 2019
- Journal Title
- Proceedings of the International Conference on Compliers, Architectures and Synthesis for Embedded Systems Companion, CASES 2019
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/11858
- DOI
- 10.1145/3349569.3351534
- ISSN
- 0000-0000
- Abstract
- 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. © 2019 Association for Computing Machinery.
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Collections - Information and Communication Engineering > Department of Semiconductor Systems Engineering > 1. Journal Articles
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