A high-performance dataflow-centric optimization framework for deep learning inference on the edge

Edge computing has been emerging as a popular scenario for model inference. However, the inference performance on edge devices (e.g., Multi-Core DSP, FGPA, etc.) suffers from inefficiency due to the lack of highly optimized inference frameworks. Previous model inference frameworks are mainly develop...

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Vydané v:	Journal of systems architecture Ročník 152; s. 103180
Hlavní autori:	Zhang, Runhua, Jiang, Hongxu, Geng, Jinkun, Tian, Fangzheng, Ma, Yuhang, Wang, Haojie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.07.2024
Predmet:	Computation graph Data locality Dataflow-centric Edge computing Model inference Computation graph Dataflow-centric Data locality Model inference Edge computing
ISSN:	1383-7621, 1873-6165
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