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Volume: 33 | Article ID: art00016
Evaluation of semi-frozen semi-fixed neural network for efficient computer vision inference
  DOI :  10.2352/ISSN.2470-1173.2021.17.AVM-213  Published OnlineJanuary 2021

Deep neural networks have been utilized in an increasing number of computer vision tasks, demonstrating superior performance. Much research has been focused on making deep networks more suitable for efficient hardware implementation, for low-power and low-latency real-time applications. In [1], Isikdogan et al. introduced a deep neural network design that provides an effective trade-off between flexibility and hardware efficiency. The proposed solution consists of fixed-topology hardware blocks, with partially frozen/partially trainable weights, that can be configured into a full network. Initial results in a few computer vision tasks were presented in [1]. In this paper, we further evaluate this network design by applying it to several additional computer vision use cases and comparing it to other hardware-friendly networks. The experimental results presented here show that the proposed semi-fixed semi-frozen design achieves competitive performanc on a variety of benchmarks, while maintaining very high hardware efficiency.

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Chyuan-Tyng Wu, Peter van Beek, Phillip Schmidt, Joao Peralta Moreira, Thomas R. Gardos, "Evaluation of semi-frozen semi-fixed neural network for efficient computer vision inferencein Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines,  2021,  pp 213-1 - 213-7,

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