Abstract
Deep learning and convolutional neural network (CNN) have been shown to solve image classification problems fast and with high accuracy. However, these algorithms tend to be very computationally intensive and resource hungry, hence making them difficult to use on embedded devices. Towards this end, we need system-level models for analysis and simulation. In this report, we describe a newly designed untimed SystemC model of GoogLeNet, a state-of-the-art deep CNN using OpenCV library. The SystemC model is automatically created from a Caffe model using a generator tool. We successfully validate the functionality of the model using Accellera SystemC 2.3.1 simulator. Then, we use RISC (Recoding Infrastructure for SystemC) to speed up the simulation by exploiting thread-level parallelism and report extensive experimental results.
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Notes
- 1.
OpenCV has built with -O0 flag meaning (almost) no compiler optimizations.
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Arasteh, E.M., Dömer, R. (2023). An Untimed SystemC Model of GoogLeNet. In: Wehrmeister, M.A., Kreutz, M., Götz, M., Henkler, S., Pimentel, A.D., Rettberg, A. (eds) Analysis, Estimations, and Applications of Embedded Systems. IESS 2019. IFIP Advances in Information and Communication Technology, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-031-26500-6_10
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