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Approximate Acceleration for CNN-based Applications on IoT Edge Devices | IEEE Conference Publication | IEEE Xplore

Approximate Acceleration for CNN-based Applications on IoT Edge Devices


Abstract:

Machine learning based sub-systems are increasingly becoming part of IoT edge devices, thereby requiring resource-efficient architectures and implementations, especially ...Show More

Abstract:

Machine learning based sub-systems are increasingly becoming part of IoT edge devices, thereby requiring resource-efficient architectures and implementations, especially when subjected to battery-constrained scenarios. The non-exact nature of Convolutional Neural Networks (CNNs) opens the possibility to use approximate computations to reduce their required runtime and energy consumption on resource-constrained IoT edge devices without significantly compromising their classification output. In this paper, we propose a resilience exploration method and a novel approximate accelerator to speed up the execution of the convolutional layer, which is the most time consuming component of CNNs, for IoT edge devices. Trained CNNs with Caffe framework are executed on a System-on-Chip with reconfigurable hardware available, where the approximate accelerator is deployed. CNN applications developed with Caffe can take advantage of our proposed approximate acceleration to execute them on IoT edge devices.
Date of Conference: 25-28 February 2020
Date Added to IEEE Xplore: 16 April 2020
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Conference Location: San Jose, Costa Rica
Citations are not available for this document.

Cites in Papers - |

Cites in Papers - IEEE (10)

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1.
Shubham Garg, Kanika Monga, Nitin Chaturvedi, S. Gurunarayanan, "Tunable Energy-Efficient Approximate Circuits for Self-Powered AI and Autonomous Edge Computing Systems", IEEE Access, vol.13, pp.43607-43630, 2025.
2.
P Thejaswini, Gautham Suresh, V. Chiraag, Sukumar Nandi, "Approximate CNN Hardware Accelerators for Resource Constrained Devices", IEEE Access, vol.13, pp.12542-12553, 2025.
3.
Ahmed Aqeel Shaikh, Anand Kumar Mukhopadhyay, Soumyajit Poddar, Suman Samui, "Toward Robust and Accurate Myoelectric Controller Design Based on Multiobjective Optimization Using Evolutionary Computation", IEEE Sensors Journal, vol.24, no.5, pp.6418-6429, 2024.
4.
Adnan Ashraf, Zhao Qingjie, Waqas Haider Khan Bangyal, Muddesar Iqbal, "Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things", IEEE Transactions on Consumer Electronics, vol.70, no.1, pp.4478-4489, 2024.
5.
Tiago Almeida, Isaías Felzmann, Lucas Wanner, "Experimental analysis of the symmetry of approximate adder designs in FPGA and ASIC", 2023 XIII Brazilian Symposium on Computing Systems Engineering (SBESC), pp.1-6, 2023.
6.
Guilherme Korol, Michael Guilherme Jordan, Mateus Beck Rutzig, Antonio Carlos Schneider Beck, "ConfAx: Exploiting Approximate Computing for Configurable FPGA CNN Acceleration at the Edge", 2022 IEEE International Symposium on Circuits and Systems (ISCAS), pp.1650-1654, 2022.
7.
Tomás González-Aragón, Jorge Castro-Godínez, "Improving Performance of Error-Tolerant Applications: A Case Study of Approximations on an Off-the-Shelf Neural Accelerator", 2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI), pp.1-6, 2021.
8.
Lucas Klemmer, Saman Froehlich, Rolf Drechsler, Daniel Große, "XbNN: Enabling CNNs on Edge Devices by Approximate On-Chip Dot Product Encoding", 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pp.1-5, 2021.
9.
Jorge Castro-Godínez, Humberto Barrantes-García, Muhammad Shafique, Jörg Henkel, "AxLS: A Framework for Approximate Logic Synthesis Based on Netlist Transformations", IEEE Transactions on Circuits and Systems II: Express Briefs, vol.68, no.8, pp.2845-2849, 2021.
10.
Vojtech Mrazek, Lukas Sekanina, Zdenek Vasicek, "Libraries of Approximate Circuits: Automated Design and Application in CNN Accelerators", IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol.10, no.4, pp.406-418, 2020.

Cites in Papers - Other Publishers (4)

1.
Hans Jakob Damsgaard, Aleksandr Ometov, Jari Nurmi, "Approximation Opportunities in Edge Computing Hardware: A Systematic Literature Review", ACM Computing Surveys, vol.55, no.12, pp.1, 2023.
2.
Zainab Aizaz, Kavita Khare, Aizaz Tirmizi, "Exploiting Pixel Redundancy and Approximate Computing for Efficient Hardware?Software Co-design of CNN on IoT Edge Devices", Proceedings of Third Emerging Trends and Technologies on Intelligent Systems, vol.730, pp.569, 2023.
3.
Manikandan Nagarajan, Rajappa Muthaiah, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, "Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model", Computational Intelligence and Neuroscience, vol.2022, pp.1, 2022.
4.
Jorge Castro-Godinez, Julian Mateus-Vargas, Muhammad Shafique, Jorg Henkel, "AxHLS", Proceedings of the 39th International Conference on Computer-Aided Design, pp.1, 2020.

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