A Low-Cost Neural ODE with Depthwise Separable Convolution for Edge Domain Adaptation on FPGAs

High-performance deep neural network (DNN)-based systems are in high demand in edge environments. Due to its high computational complexity, it is challenging to deploy DNNs on edge devices with strict limitations on computational resources. In this paper, we derive a compact while highly-accurate DN...

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Veröffentlicht in:	IEICE Transactions on Information and Systems 2023/07/01, Vol.E106.D(7), pp.1186-1197
Hauptverfasser:	KAWAKAMI, Hiroki, WATANABE, Hirohisa, SUGIURA, Keisuke, MATSUTANI, Hiroki
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Adaptation Artificial neural networks Convolution Differential equations distillation domain adaptation edge device Feature maps Field programmable gate arrays FPGA Image classification Inference neural ODE Ordinary differential equations Parameters Resource utilization
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