PAIBoard: A Neuromorphic Computing Platform for Hybrid Neural Networks in Robot Dog Application

Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips...

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Published inElectronics (Basel) Vol. 13; no. 18; p. 3619
Main Authors Chen, Guang, Cao, Jian, Zou, Chenglong, Feng, Shuo, Zhong, Yi, Zhang, Xing, Wang, Yuan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2024
Subjects
Algorithms
Artificial intelligence
Artificial neural networks
Brain
Communication
Design
Dogs
Energy efficiency
Interdisciplinary subjects
Modules
Nervous system
Neural networks
Neuromorphic computing
Neurons
Neurosciences
Obstacle avoidance
Robots
Software
Tracking
Transceivers
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Abstract Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips to support HNNs, but developing platforms to advance neuromorphic computing systems is equally essential. This paper presents the PAIBoard platform, which is designed to facilitate the implementation of HNNs. The platform comprises three main components: the upper computer, the communication module, and the neuromorphic computing chip. Both hardware and software performance measurements indicate that our platform achieves low power consumption, high energy efficiency and comparable task accuracy. Furthermore, PAIBoard is applied in a robot dog for tracking and obstacle avoidance system. The tracking module combines data from ultra-wide band (UWB) transceivers and vision, while the obstacle avoidance module utilizes depth information from an RGB-D camera, which further underscores the potential of our platform to tackle challenging tasks in real-world applications.
AbstractList Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips to support HNNs, but developing platforms to advance neuromorphic computing systems is equally essential. This paper presents the PAIBoard platform, which is designed to facilitate the implementation of HNNs. The platform comprises three main components: the upper computer, the communication module, and the neuromorphic computing chip. Both hardware and software performance measurements indicate that our platform achieves low power consumption, high energy efficiency and comparable task accuracy. Furthermore, PAIBoard is applied in a robot dog for tracking and obstacle avoidance system. The tracking module combines data from ultra-wide band (UWB) transceivers and vision, while the obstacle avoidance module utilizes depth information from an RGB-D camera, which further underscores the potential of our platform to tackle challenging tasks in real-world applications.
Audience Academic
Author Zou, Chenglong
Chen, Guang
Zhang, Xing
Wang, Yuan
Cao, Jian
Feng, Shuo
Zhong, Yi
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SubjectTerms Algorithms
Artificial intelligence
Artificial neural networks
Brain
Communication
Design
Dogs
Energy efficiency
Interdisciplinary subjects
Modules
Nervous system
Neural networks
Neuromorphic computing
Neurons
Neurosciences
Obstacle avoidance
Robots
Software
Tracking
Transceivers
Title PAIBoard: A Neuromorphic Computing Platform for Hybrid Neural Networks in Robot Dog Application
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Volume 13
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