Details
Presenter(s)
![Hana Kim Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/23251.png?h=df1b6c88&itok=71GaBamE)
Display Name
Hana Kim
- Affiliation
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AffiliationEwha Womans University
- Country
Abstract
In this paper, we present two simple methods for fast analysis in mixed-precision determination and for computational complexity reduction in inference, respectively. With the proposed SQNR-based analysis, we can significantly reduce the time required in mixed-precision scheme determination compared to conventional mAP-based scheme with negligible loss of accuracy. Also, by combining the hyperparameters of the target neural networks in the process of mixed-precision determination, we can reduce the computational complexity in inference. We applied these two proposed methods to the SSDlite network with MobileNet-v2 and YOLOv2 network and evaluate the quantized networks with the proposed mixed-precision schemes on the Pascal-VOC dataset.