Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147717
Title: Accelerating computer vision algorithms on heterogeneous edge computing platforms
Authors: Prakash, Alok
Ramakrishnan, Nirmala
Garg, Kratika
Srikanthan, Thambipillai
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Prakash, A., Ramakrishnan, N., Garg, K. & Srikanthan, T. (2020). Accelerating computer vision algorithms on heterogeneous edge computing platforms. 2020 IEEE Workhop on Signal Processing Systems (SiPS), 2020-October, 1-6. https://dx.doi.org/10.1109/SiPS50750.2020.9195221
Project: NRF TUMCREATE
Abstract: Heterogeneity has become the cornerstone of modern embedded System-on-Chips (SoCs), used in latest smart-phones and edge computing platforms to achieve high performance under tight power budgets. Alongside the general purpose multi-core CPUs, such SoCs typically integrate several specialized processing elements such as GPU and DSP to ensure power-efficient execution of specific workloads. In the past, many computer vision algorithms and their kernels have been shown to benefit from execution on GPUs, both in terms of performance and power consumption. Existing work has also demonstrated the benefit of accelerating them simultaneously on multi-core CPUs and discrete as well as integrated GPUs found in PCs and workstations. Recently, authors have also focused on accelerating such applications on heterogeneous embedded SoCs with integrated CPU and GPU. In this paper, we first present an extensive literature review of such efforts and highlight their strengths and limitations. Next, we use the latest state-of-the-art edge computing platform, Odroid-N2, and the older Odroid-XU3 platform, both of which use heterogeneous embedded SoCs, to explore the acceleration of the convolution kernel with different filter sizes and its impact on the KLT tracking algorithm. Lastly, we discuss the challenges and opportunities in leveraging such SoCs for computer vision and other AI applications.
URI: https://hdl.handle.net/10356/147717
ISBN: 9781728180991
DOI: 10.1109/SiPS50750.2020.9195221
Rights: © 2020 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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