A neuromorphic SLAM architecture using gated-memristive synapses

Jones, Alexander; Rushton, Gérard; Merkel, Cory; Herrmann, Eric; Jacob, Ajey P.; Thiem, Clare; Jha, Rashmi

doi:10.1016/j.neucom.2019.09.098

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…In general, the application of neuromorphic systems has been commonly explored in the aforementioned fields as it is found to bring improvement in energy efficiency and performance as compared to a traditional computing platform [171][172][173]247]. This has led researchers to explore the incorporation of neuromorphic systems in some of the SLAM implementations such as in [24,[247][248][249][250][251], resulting in enhanced energy efficiency and performance in addition to other benefits. In [24], when the system (which represented the robot's 4DoF pose in a 3D environment) was integrated with a lightweight vision system (in a similar manner to the vision system of mammalian), the system could generate comprehensive 3D experience maps with consistency both for simulated and real 3D environments.…”

Section: Neuromorphic Computing In Slammentioning

confidence: 99%

“…In [24], when the system (which represented the robot's 4DoF pose in a 3D environment) was integrated with a lightweight vision system (in a similar manner to the vision system of mammalian), the system could generate comprehensive 3D experience maps with consistency both for simulated and real 3D environments. Using the self-learning hardware architecture (gated-memristive device) in conjunction with the spiking neurons, the SLAM system was successful in making navigation-related operations in a simple environment consuming minimal power (36µW) [248]. Similarly, the research by [247,250] has shown that power consumption is minimal when the system employs the pose-cell array and digital head direction cell, which mimics place and head direction cells of the rodent brain respectively.…”

Section: Neuromorphic Computing In Slammentioning

confidence: 99%

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Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey

Tenzin,

Rassau,

Chai

2024

Preprint

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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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Section: Neuromorphic Computing In Slammentioning

confidence: 99%

Section: Neuromorphic Computing In Slammentioning

confidence: 99%

Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey

Tenzin,

Rassau,

Chai

2024

Preprint

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“…By considering the robot’s embodied experiences and interactions, SLAM provides a concrete implementation of embodied cognition principles in the domain of robotic perception and spatial awareness. The use of SNNs for SLAM problems on neuromorphic chips is an active area of research [ 105 , 106 ], especially as perceptual sensors and models of sensory integration improve. SNNs can process sensor data to extract features and recognize objects, while their temporal and parallel processing capabilities enable efficient representation of sensory information.…”

Section: Snn Embodiment Into Cognitive Robotic Systemsmentioning

confidence: 99%

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

Pham,

D’Angiulli,

Dehnavi

et al. 2023

Brain Sciences

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We examine the challenging “marriage” between computational efficiency and biological plausibility—A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man’s land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics.

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“…Two-terminal NVMs such as ReRAM integrated with MOSFET selectors are called 1T1R cells. Other NVMs, such as FeFETs 128 and other gated synaptic devices, 12 employ a three-terminal structure, so their selector is "built-in." Neuromorphic chips based on NVM crossbars have been demonstrated for vision tasks, such as classification [129][130][131] and several others.…”

Section: Memory Technologies For Spike-based Neuromorphic Computingmentioning

confidence: 99%

“…In contrast, our brains represent information in the patterns of neuron action potentials, or "spikes," using noisy mixed signal hardware, and have closely coupled memory and processing units. The space of neuromorphic computing is expansive, and the term has been used to describe designs lying anywhere between these two extremes for applications spanning object/gesture recognition, 9 image reconstruction, 10 star tracking, 11 simultaneous localization and mapping, 12 surveillance, and monitoring. 13 In this paper, we review neuromorphic computing as it applies to brain-inspired vision.…”

Section: Introductionmentioning

confidence: 99%

Review of spike-based neuromorphic computing for brain-inspired vision: biology, algorithms, and hardware

Hendy

Merkel

2022

J. Electron. Imag.

Self Cite

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Neuromorphic computing is becoming a popular approach for implementations of brain-inspired machine learning tasks. As a paradigm for both hardware and algorithm design, neuromorphic computing aims to emulate several aspects related to the structure and function of the biological nervous system to achieve artificial intelligence with efficiencies that are orders of magnitude better than those exhibited by general-purpose computing hardware. We provide a holistic treatment of spike-based neuromorphic computing (i.e., based on spiking neural networks), detailing biological motivation, key aspects of neuromorphic algorithms, and a survey of state-of-the-art neuromorphic hardware. In particular, we focus on these aspects within the context of brain-inspired vision applications. Our aim is to serve as a complement to several of the existing reviews on neuromorphic computing while also providing a unique perspective.

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A neuromorphic SLAM architecture using gated-memristive synapses

Cited by 9 publications

References 34 publications

Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey

Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

Review of spike-based neuromorphic computing for brain-inspired vision: biology, algorithms, and hardware

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