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Tenzin S, Rassau A, Chai D. Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey. Biomimetics (Basel) 2024; 9:444. [PMID: 39056885 PMCID: PMC11274992 DOI: 10.3390/biomimetics9070444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 07/15/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024] Open
Abstract
Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance, was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.
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Affiliation(s)
| | - Alexander Rassau
- School of Engineering, Edith Cowan University, Perth, WA 6027, Australia; (S.T.); (D.C.)
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Sun P, Chua Y, Devos P, Botteldooren D. Learnable axonal delay in spiking neural networks improves spoken word recognition. Front Neurosci 2023; 17:1275944. [PMID: 38027508 PMCID: PMC10665570 DOI: 10.3389/fnins.2023.1275944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Spiking neural networks (SNNs), which are composed of biologically plausible spiking neurons, and combined with bio-physically realistic auditory periphery models, offer a means to explore and understand human auditory processing-especially in tasks where precise timing is essential. However, because of the inherent temporal complexity in spike sequences, the performance of SNNs has remained less competitive compared to artificial neural networks (ANNs). To tackle this challenge, a fundamental research topic is the configuration of spike-timing and the exploration of more intricate architectures. In this work, we demonstrate a learnable axonal delay combined with local skip-connections yields state-of-the-art performance on challenging benchmarks for spoken word recognition. Additionally, we introduce an auxiliary loss term to further enhance accuracy and stability. Experiments on the neuromorphic speech benchmark datasets, NTIDIDIGITS and SHD, show improvements in performance when incorporating our delay module in comparison to vanilla feedforward SNNs. Specifically, with the integration of our delay module, the performance on NTIDIDIGITS and SHD improves by 14% and 18%, respectively. When paired with local skip-connections and the auxiliary loss, our approach surpasses both recurrent and convolutional neural networks, yet uses 10 × fewer parameters for NTIDIDIGITS and 7 × fewer for SHD.
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Affiliation(s)
- Pengfei Sun
- Department of Information Technology, WAVES Research Group, Ghent University, Ghent, Belgium
| | - Yansong Chua
- Neuromorphic Computing Laboratory, China Nanhu Academy of Electronics and Information Technology, Jiaxing, China
| | - Paul Devos
- Department of Information Technology, WAVES Research Group, Ghent University, Ghent, Belgium
| | - Dick Botteldooren
- Department of Information Technology, WAVES Research Group, Ghent University, Ghent, Belgium
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Fang W, Chen Y, Ding J, Yu Z, Masquelier T, Chen D, Huang L, Zhou H, Li G, Tian Y. SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence. SCIENCE ADVANCES 2023; 9:eadi1480. [PMID: 37801497 PMCID: PMC10558124 DOI: 10.1126/sciadv.adi1480] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 09/05/2023] [Indexed: 10/08/2023]
Abstract
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.
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Affiliation(s)
- Wei Fang
- School of Computer Science, Peking University, China
- Peng Cheng Laboratory, China
- School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, China
| | - Yanqi Chen
- School of Computer Science, Peking University, China
- Peng Cheng Laboratory, China
| | - Jianhao Ding
- School of Computer Science, Peking University, China
| | - Zhaofei Yu
- Institute for Artificial Intelligence, Peking University, China
| | - Timothée Masquelier
- Centre de Recherche Cerveau et Cognition (CERCO), UMR5549 CNRS–Université Toulouse 3, France
| | - Ding Chen
- Peng Cheng Laboratory, China
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China
| | - Liwei Huang
- School of Computer Science, Peking University, China
- Peng Cheng Laboratory, China
| | | | - Guoqi Li
- Institute of Automation, Chinese Academy of Sciences, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Yonghong Tian
- School of Computer Science, Peking University, China
- Peng Cheng Laboratory, China
- School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, China
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Liu S, Leung VCH, Dragotti PL. First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures. Front Neurosci 2023; 17:1266003. [PMID: 37849889 PMCID: PMC10577212 DOI: 10.3389/fnins.2023.1266003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/11/2023] [Indexed: 10/19/2023] Open
Abstract
Spiking neural networks (SNNs) are well-suited to process asynchronous event-based data. Most of the existing SNNs use rate-coding schemes that focus on firing rate (FR), and so they generally ignore the spike timing in events. On the contrary, methods based on temporal coding, particularly time-to-first-spike (TTFS) coding, can be accurate and efficient but they are difficult to train. Currently, there is limited research on applying TTFS coding to real events, since traditional TTFS-based methods impose one-spike constraint, which is not realistic for event-based data. In this study, we present a novel decision-making strategy based on first-spike (FS) coding that encodes FS timings of the output neurons to investigate the role of the first-spike timing in classifying real-world event sequences with complex temporal structures. To achieve FS coding, we propose a novel surrogate gradient learning method for discrete spike trains. In the forward pass, output spikes are encoded into discrete times to generate FS times. In the backpropagation, we develop an error assignment method that propagates error from FS times to spikes through a Gaussian window, and then supervised learning for spikes is implemented through a surrogate gradient approach. Additional strategies are introduced to facilitate the training of FS timings, such as adding empty sequences and employing different parameters for different layers. We make a comprehensive comparison between FS and FR coding in the experiments. Our results show that FS coding achieves comparable accuracy to FR coding while leading to superior energy efficiency and distinct neuronal dynamics on data sequences with very rich temporal structures. Additionally, a longer time delay in the first spike leads to higher accuracy, indicating important information is encoded in the timing of the first spike.
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Affiliation(s)
- Siying Liu
- Communications and Signal Processing Group, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
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Jiang J, Tian F, Liang J, Shen Z, Liu Y, Zheng J, Wu H, Zhang Z, Fang C, Zhao Y, Shi J, Xue X, Zeng X. MSPAN: A Memristive Spike-Based Computing Engine With Adaptive Neuron for Edge Arrhythmia Detection. Front Neurosci 2021; 15:761127. [PMID: 34975373 PMCID: PMC8715923 DOI: 10.3389/fnins.2021.761127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Abstract
In this work, a memristive spike-based computing in memory (CIM) system with adaptive neuron (MSPAN) is proposed to realize energy-efficient remote arrhythmia detection with high accuracy in edge devices by software and hardware co-design. A multi-layer deep integrative spiking neural network (DiSNN) is first designed with an accuracy of 93.6% in 4-class ECG classification tasks. Then a memristor-based CIM architecture and the corresponding mapping method are proposed to deploy the DiSNN. By evaluation, the overall system achieves an accuracy of over 92.25% on the MIT-BIH dataset while the area is 3.438 mm2 and the power consumption is 0.178 μJ per heartbeat at a clock frequency of 500 MHz. These results reveal that the proposed MSPAN system is promising for arrhythmia detection in edge devices.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Xiaoyong Xue
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, China
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Debat G, Chauhan T, Cottereau BR, Masquelier T, Paindavoine M, Baures R. Event-Based Trajectory Prediction Using Spiking Neural Networks. Front Comput Neurosci 2021; 15:658764. [PMID: 34108870 PMCID: PMC8180888 DOI: 10.3389/fncom.2021.658764] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory by adding a simple read-out layer composed of polynomial regressions, and trained in a supervised manner. Hence, we show that a SNN receiving inputs from an event-based sensor can extract relevant spatio-temporal patterns to process and predict ball trajectories.
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Affiliation(s)
- Guillaume Debat
- CERCO UMR 5549, CNRS-Université Toulouse 3, Toulouse, France
| | - Tushar Chauhan
- CERCO UMR 5549, CNRS-Université Toulouse 3, Toulouse, France
| | | | | | - Michel Paindavoine
- Laboratory for Research on Learning and Development (LEAD), University of Burgundy, CNRS UMR, Dijon, France
| | - Robin Baures
- CERCO UMR 5549, CNRS-Université Toulouse 3, Toulouse, France
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