An Untimed SystemC Model of GoogLeNet - IFIP Advances in Information and Communication Technology Access content directly
Conference Papers Year : 2023

An Untimed SystemC Model of GoogLeNet

Emad Malekzadeh Arasteh
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Rainer Dömer
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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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hal-04489017 , version 1 (04-03-2024)

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Emad Malekzadeh Arasteh, Rainer Dömer. An Untimed SystemC Model of GoogLeNet. 6th International Embedded Systems Symposium (IESS), Sep 2019, Friedrichshafen, Germany. pp.117-129, ⟨10.1007/978-3-031-26500-6_10⟩. ⟨hal-04489017⟩
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