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Neshatpour, Katayoun ; Behnia, Farnaz ; Homayoun, Houman ; Sasan, Avesta ( , 20th International Symposium on Quality Electronic Design (ISQED))One of the promising solutions for energy-efficient CNNs is to break them down into multiple stages that are executed sequentially (MS-CNN). In this paper, we illustrate that unlike deep CNNs, MS-CNNs develop a form of contextual awareness of input data in initial stages, which could be used to dynamically change the structure and connectivity of such networks to reduce their computational complexity, making them a better fit for low-power and real-time systems. We suggest three run-time optimization policies, which are capable of exploring such contextual knowledge, and illustrate how the proposed policies construct a dynamic architecture suitable for a wide range of applications with varied accuracy requirements, resources, and time-budget, without further need for network re-training. Moreover, we propose variable and dynamic bit-length fixed-point conversion to further reduce the memory footprint of the MS-CNNs.more » « less
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Neshatpour, Katayoun ; Behnia, Farnaz ; Homayoun, Houman ; Sasan, Avesta ( , 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE))With Convolutional Neural Networks (CNN) becoming more of a commodity in the computer vision field, many have attempted to improve CNN in a bid to achieve better accuracy to a point that CNN accuracies have surpassed that of human's capabilities. However, with deeper networks, the number of computations and consequently the power needed per classification has grown considerably. In this paper, we propose Iterative CNN (ICNN) by reformulating the CNN from a single feed-forward network to a series of sequentially executed smaller networks. Each smaller network processes a sub-sample of input image, and features extracted from previous network, and enhances the classification accuracy. Upon reaching an acceptable classification confidence, ICNN immediately terminates. The proposed network architecture allows the CNN function to be dynamically approximated by creating the possibility of early termination and performing the classification with far fewer operations compared to a conventional CNN. Our results show that this iterative approach competes with the original larger networks in terms of accuracy while incurring far less computational complexity by detecting many images in early iterations.more » « less