Live demonstration: A hardware system for emulating the early vision utilizing a silicon retina and SpiNNaker chips

Kawasetsu, Takumi; Ishida, Ryuichi; Sanada, Tadashi; Okuno, Hirotsugu

doi:10.1109/biocas.2014.6981691

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…As reported by Gutierrez-Galan et al [144], SpiNNaker was used to classify audio information to generate gait for a hexapod robot. Kawasetsu et al [145] used SpiNNaker to simulate a neural activity in the retina and visual cerebral cortex. Meanwhile, Stromatias et al [146] used SpiNNaker to implement a deep belief network for handwriting digit recognition.…”

Section: Tactile Neuromorphic Computingmentioning

confidence: 99%

Embodied tactile perception and learning

Liu

Guo

Sun

et al. 2020

Brain Science Advances

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Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

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Section: Tactile Neuromorphic Computingmentioning

confidence: 99%

Embodied tactile perception and learning

Liu

Guo

Sun

et al. 2020

Brain Science Advances

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“…The SpiNNaker architecture is highly flexible, however it suffers from excessively high power consumption when compared to other approaches. SpiNNakers key strength is in its ability to handle arbitrary local and global connections, in other words it supports full arbitrary connectivity between any neurons, and it has been used in a range of applications including tasks such as vision systems [11]. Other example neuromorphic systems are TrueNorth and Neurogrid.…”

Section: Implementing Neural Architecturesmentioning

confidence: 99%

A Reconfigurable Architecture for Implementing Locally Connected Neural Arrays

G-H-Cater

Clarke

Metcalfe

et al. 2018

Advances in Intelligent Systems and Computing

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Moores law is rapidly approaching a long-predicted decline, and with it the performance gains of conventional processors are becoming ever more marginal. Cognitive computing systems based on neural networks have the potential to provide a solution to the decline of Moores law. Identifying common traits in neural systems can lead to the design of more efficient, robust and adaptable processors. Despite the potentials, large-scale neural systems remain difficult to implement due to constraints on scalability. Here we introduce a new hardware architecture for implementing locally connected neural networks that can model biological systems with a high level of scalability. We validate our architecture using a full model of the locomotion system of the Caenorhabditis elegans. Further, we show that our proposed architecture archives a 9 fold increase in clock speed over existing hardware models. Importantly the clock speed for our architecture is found to be independent of system size, providing an unparalleled level of scalability. Our approach can be applied to the modelling of large neural networks, with greater performance, easier configuration and a high level of scalability.

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Live demonstration: A hardware system for emulating the early vision utilizing a silicon retina and SpiNNaker chips

Cited by 2 publications

References 2 publications

Embodied tactile perception and learning

Embodied tactile perception and learning

A Reconfigurable Architecture for Implementing Locally Connected Neural Arrays

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