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Gyro: A Digital Spiking Neural Network Architecture for Multi-Sensory Data Analytics

Authors:

Federico Corradi,

Guido Adriaans,

Sander StuijkAuthors Info & Claims

DroneSE and RAPIDO '21: Proceedings of the 2021 Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools Proceedings

Pages 9 - 15

https://doi.org/10.1145/3444950.3444951

Published: 24 February 2021 Publication History

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Abstract

Unmanned Aerial Vehicles (UAVs) that interact with the physical world in real-time make use of a multitude of sensors and often execute deep neural network workloads for perceiving the state of the environment. To increase UAV’s operations, it is required to execute these workloads in the most power-efficient manner. Spiking Neural Networks (SNNs) have been proposed as an alternative solution for the execution of deep neural networks in an energy-efficient way. We introduce Gyro, a digital event-driven architecture capable of executing spiking neural networks. The architecture is tailored towards sensory fusion applications and it is optimized for Field-Programmable Gate Arrays (FPGAs). In hardware, we demonstrate the performance of a sensory fusion task using a public dataset of bi-temporal optical-radar data for pixel-wise crop classification. We achieve an accuracy of 99,7%, a peak throughput of 31,82 Giga Synaptic Operations per Second (GSOPS) while consuming 50 pico Joule / Synaptic Operation (pJ/SO).

References

[1]

Adarsha Balaji, Federico Corradi, Anup Das, Sandeep Pande, Siebren Schaafsma, and Francky Catthoor. 2018. Power-accuracy trade-offs for heartbeat classification on neural networks hardware. Journal of Low Power Electronics 14, 4 (2018), 508–519.

[2]

Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, 2018. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 1 (2018), 82–99.

[3]

Peter Dayan and Laurence F Abbott. 2001. Theoretical neuroscience: computational and mathematical modeling of neural systems. Computational Neuroscience Series.

[4]

Michael V DeBole, Brian Taba, Arnon Amir, Filipp Akopyan, Alexander Andreopoulos, William P Risk, Jeff Kusnitz, Carlos Ortega Otero, Tapan K Nayak, Rathinakumar Appuswamy, 2019. TrueNorth: Accelerating from zero to 64 million neurons in 10 years. Computer 52, 5 (2019), 20–29.

[5]

Peter U Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, and Michael Pfeiffer. 2015. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In 2015 International Joint Conference on Neural Networks (IJCNN). ieee, 1–8.

[6]

J. Han, Z. Li, W. Zheng, and Y. Zhang. 2020. Hardware implementation of spiking neural networks on FPGA. Tsinghua Science and Technology 25, 4 (2020), 479–486.

[7]

Mahmoud Hussein, Réda Nouacer, Yassine Ouhammou, Eugenio Villar, Federico Corradi, Carlo Tieri, and Rodrigo Castiñeira. 2020. Key Enabling Technologies for Drones. In 2020 23rd Euromicro Conference on Digital System Design (DSD). IEEE, 489–496.

[8]

Iman Khosravi. 2018. Crop mapping using fused optical-radar data set Data Set. https://archive.ics.uci.edu/ml/datasets/Crop+mapping+using+fused+optical-radar+data+set

[9]

Iman Khosravi and Seyed Kazem Alavipanah. 2019. A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations. International Journal of Remote Sensing 40, 18 (2019), 7221–7251.

[10]

Iman Khosravi, Abdolreza Safari, and Saeid Homayouni. 2018. MSMD: maximum separability and minimum dependency feature selection for cropland classification from optical and radar data. International Journal of Remote Sensing 39, 8 (2018), 2159–2176.

[11]

I. Kiselev, D. Neil, and S. Liu. 2016. Event-driven deep neural network hardware system for sensor fusion. In 2016 IEEE International Symposium on Circuits and Systems (ISCAS). 2495–2498. https://doi.org/10.1109/ISCAS.2016.7539099

[12]

Hesham Mostafa, Bruno U Pedroni, Sadique Sheik, and Gert Cauwenberghs. 2017. Fast classification using sparsely active spiking networks. In 2017 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 1–4.

[13]

D. Neil and S. Liu. 2014. Minitaur, an Event-Driven FPGA-Based Spiking Network Accelerator. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 22, 12 (Dec 2014), 2621–2628. https://doi.org/10.1109/TVLSI.2013.2294916

[14]

Danilo Pani, Paolo Meloni, Giuseppe Tuveri, Francesca Palumbo, Paolo Massobrio, and Luigi Raffo. 2017. An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks. Frontiers in Neuroscience 11 (2017), 90. https://doi.org/10.3389/fnins.2017.00090

[15]

Harry A Pierson and Michael S Gashler. 2017. Deep learning in robotics: a review of recent research. Advanced Robotics 31, 16 (2017), 821–835.

[16]

Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy. 2019. Going deeper in spiking neural networks: Vgg and residual architectures. Frontiers in neuroscience 13 (2019), 95.

[17]

Georgios Smaragdos, Sebastian Isaza, Martijn F van Eijk, Ioannis Sourdis, and Christos Strydis. 2014. FPGA-based biophysically-meaningful modeling of olivocerebellar neurons. In Proceedings of the 2014 ACM/SIGDA international symposium on Field-programmable gate arrays. 89–98.

Digital Library

[18]

Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothée Masquelier, and Anthony Maida. 2019. Deep learning in spiking neural networks. Neural Networks 111(2019), 47–63.

Digital Library

[19]

Runchun M Wang, Chetan S Thakur, and André van Schaik. 2018. An FPGA-based massively parallel neuromorphic cortex simulator. Frontiers in neuroscience 12 (2018), 213.

Cited By

Corradi FShen ZZhao HAlachiotis N(2024)Accelerated Spiking Convolutional Neural Networks for Scalable Population GenomicsProceedings of the 14th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies10.1145/3665283.3665285(53-62)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3665283.3665285
Ullah SSahoo SKumar A(2024) AxOSpike : Spiking Neural Networks-Driven Approximate Operator Design IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.344300043:11(3324-3335)Online publication date: Nov-2024
https://doi.org/10.1109/TCAD.2024.3443000
Matinizadeh SMohammadhassani APacik-Nelson NPolykretisl IMishra AShackleford JKandasamy NGallo EDas A(2024)A Fully-Configurable Digital Spiking Neuromorphic Hardware Design with Variable Quantization and Mixed Precision2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS)10.1109/MWSCAS60917.2024.10658724(937-941)Online publication date: 11-Aug-2024
https://doi.org/10.1109/MWSCAS60917.2024.10658724
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Gyro: A Digital Spiking Neural Network Architecture for Multi-Sensory Data Analytics
1. Computer systems organization
  1. Architectures
    1. Other architectures
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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DroneSE and RAPIDO '21: Proceedings of the 2021 Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools Proceedings

January 2021

73 pages

ISBN:9781450389525

DOI:10.1145/3444950

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 24 February 2021

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ECSEL H2020

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DroneSE and RAPIDO '21

DroneSE and RAPIDO '21: Methods and Tools

January 18 - 20, 2021

Budapest, Hungary

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Overall Acceptance Rate 14 of 28 submissions, 50%

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Cited By

Corradi FShen ZZhao HAlachiotis N(2024)Accelerated Spiking Convolutional Neural Networks for Scalable Population GenomicsProceedings of the 14th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies10.1145/3665283.3665285(53-62)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3665283.3665285
Ullah SSahoo SKumar A(2024) AxOSpike : Spiking Neural Networks-Driven Approximate Operator Design IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.344300043:11(3324-3335)Online publication date: Nov-2024
https://doi.org/10.1109/TCAD.2024.3443000
Matinizadeh SMohammadhassani APacik-Nelson NPolykretisl IMishra AShackleford JKandasamy NGallo EDas A(2024)A Fully-Configurable Digital Spiking Neuromorphic Hardware Design with Variable Quantization and Mixed Precision2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS)10.1109/MWSCAS60917.2024.10658724(937-941)Online publication date: 11-Aug-2024
https://doi.org/10.1109/MWSCAS60917.2024.10658724
Pes LLuiken RCorradi FFrenkel C(2024)Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS)10.1109/AICAS59952.2024.10595872(41-45)Online publication date: 22-Apr-2024
https://doi.org/10.1109/AICAS59952.2024.10595872
Cherian RKanaga E G(2024)Unleashing the potential of spiking neural networks for epileptic seizure detectionNeurocomputing10.1016/j.neucom.2024.127934598:COnline publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127934
Plagwitz PHannig FTeich JKeszocze O(2024)SNN vs. CNN Implementations on FPGAs: An Empirical EvaluationApplied Reconfigurable Computing. Architectures, Tools, and Applications10.1007/978-3-031-55673-9_1(3-18)Online publication date: 10-Mar-2024
https://doi.org/10.1007/978-3-031-55673-9_1
Varshika MMishra AKandasamy NDas ATakahashi A(2023)Hardware-Software Co-Design for On-Chip Learning in AI SystemsProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568359(624-631)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3566097.3568359
Dampfhoffer MMesquida TValentian AAnghel L(2023)Are SNNs Really More Energy-Efficient Than ANNs? an In-Depth Hardware-Aware StudyIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.32145097:3(731-741)Online publication date: Jun-2023
https://doi.org/10.1109/TETCI.2022.3214509
Ayuso-Martinez ACasanueva-Morato DDominguez-Morales JJimenez-Fernandez AJimenez-Moreno G(2023)Construction of a Spike-Based Memory Using Neural-Like Logic Gates Based on Spiking Neural Networks on SpiNNakerIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.328106311:4(868-881)Online publication date: Oct-2023
https://doi.org/10.1109/TETC.2023.3281063
Khan ASingh R(2023)Surrogate Gradient-Based Medical Image Classification Using Spike Neural Network2023 International Conference on Computational Intelligence, Networks and Security (ICCINS)10.1109/ICCINS58907.2023.10450147(1-6)Online publication date: 22-Dec-2023
https://doi.org/10.1109/ICCINS58907.2023.10450147
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