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2D Materials Powering Neuromorphic Intelligence. [PDF]
Nanomicro LettKazmi J +9 more
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Artificial sparse neuron dendrites for visual information inference. [PDF]
Sci AdvWang R +10 more
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Sensors and Actuators A: Physical, 1996
Abstract A review of current trends in biologically based computational visual sensors, also known as neuromorphic sensors, is presented. Neuromorphic sensors attempt to mimic the sensing and early visual-processing characteristics of living organisms.
R Etienne-Cummings, J Van Der Spiegel
exaly +2 more sources
Abstract A review of current trends in biologically based computational visual sensors, also known as neuromorphic sensors, is presented. Neuromorphic sensors attempt to mimic the sensing and early visual-processing characteristics of living organisms.
R Etienne-Cummings, J Van Der Spiegel
exaly +2 more sources
Science, 2000
Vision is one of the most useful sensory functions, but the real-time processing of the continuous, high-dimensional input signals provided by vision sensors is a major challenge in robot design. Conventional digital vision sensors tend to have excessive power consumption, size, and cost for useful applications.
Giacomo Indiveri, Rodney Douglas
openaire +1 more source
Vision is one of the most useful sensory functions, but the real-time processing of the continuous, high-dimensional input signals provided by vision sensors is a major challenge in robot design. Conventional digital vision sensors tend to have excessive power consumption, size, and cost for useful applications.
Giacomo Indiveri, Rodney Douglas
openaire +1 more source
Spike Count Maximization for Neuromorphic Vision Recognition
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023Spiking Neural Networks (SNNs) are the promising models of neuromorphic vision recognition. The mean square error (MSE) and cross-entropy (CE) losses are widely applied to supervise the training of SNNs on neuromorphic datasets. However, the relevance between the output spike counts and predictions is not well modeled by the existing loss functions ...
Jianxiong Tang +3 more
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2023
Neuromorphic computing, referred to as brain-inspired computing for big-data processing and accelerating artificial intelligence (AI) computation, has received a significant boost from the emergence of memristors and associated computing algorithms over the past decade.
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Neuromorphic computing, referred to as brain-inspired computing for big-data processing and accelerating artificial intelligence (AI) computation, has received a significant boost from the emergence of memristors and associated computing algorithms over the past decade.
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Microsaccades for Neuromorphic Stereo Vision
2018Depth perception through stereo vision is an important feature of biological and artificial vision systems. While biological systems can compute disparities effortlessly, it requires intensive processing for artificial vision systems. The computing complexity resides in solving the correspondence problem – finding matching pairs of points in the two ...
Jacques Kaiser +8 more
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Science China Information Sciences, 2018
The paper reviews the progress of neuromorphic vision chip research in decades. It focuses on two kinds of the neuromorphic vision chips: frame-driven (FD) and event-driven (ED) vision chips. The FD and ED vision chips are very different from each other in system architecture, image sensing, image information coding, image processing algorithm, design ...
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The paper reviews the progress of neuromorphic vision chip research in decades. It focuses on two kinds of the neuromorphic vision chips: frame-driven (FD) and event-driven (ED) vision chips. The FD and ED vision chips are very different from each other in system architecture, image sensing, image information coding, image processing algorithm, design ...
openaire +1 more source
Hardware Acceleration for Neuromorphic Vision Algorithms
Journal of Signal Processing Systems, 2012Neuromorphic vision algorithms are biologically inspired models that follow the processing that takes place in the primate visual cortex. Despite their efficiency and robustness, the complexity of these algorithms results in reduced performance when executed on general purpose processors.
Ahmed Al-Maashri +6 more
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