4,026 research outputs found
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
Creating datasets for Neuromorphic Vision is a challenging task. A lack of
available recordings from Neuromorphic Vision sensors means that data must
typically be recorded specifically for dataset creation rather than collecting
and labelling existing data. The task is further complicated by a desire to
simultaneously provide traditional frame-based recordings to allow for direct
comparison with traditional Computer Vision algorithms. Here we propose a
method for converting existing Computer Vision static image datasets into
Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving
the sensor rather than the scene or image is a more biologically realistic
approach to sensing and eliminates timing artifacts introduced by monitor
updates when simulating motion on a computer monitor. We present conversion of
two popular image datasets (MNIST and Caltech101) which have played important
roles in the development of Computer Vision, and we provide performance metrics
on these datasets using spike-based recognition algorithms. This work
contributes datasets for future use in the field, as well as results from
spike-based algorithms against which future works can compare. Furthermore, by
converting datasets already popular in Computer Vision, we enable more direct
comparison with frame-based approaches.Comment: 10 pages, 6 figures in Frontiers in Neuromorphic Engineering, special
topic on Benchmarks and Challenges for Neuromorphic Engineering, 2015 (under
review
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Bio-Inspired Stereo Vision Calibration for Dynamic Vision Sensors
Many advances have been made in the eld of computer vision. Several recent research trends
have focused on mimicking human vision by using a stereo vision system. In multi-camera systems, a
calibration process is usually implemented to improve the results accuracy. However, these systems generate
a large amount of data to be processed; therefore, a powerful computer is required and, in many cases,
this cannot be done in real time. Neuromorphic Engineering attempts to create bio-inspired systems that
mimic the information processing that takes place in the human brain. This information is encoded using
pulses (or spikes) and the generated systems are much simpler (in computational operations and resources),
which allows them to perform similar tasks with much lower power consumption, thus these processes
can be developed over specialized hardware with real-time processing. In this work, a bio-inspired stereovision
system is presented, where a calibration mechanism for this system is implemented and evaluated
using several tests. The result is a novel calibration technique for a neuromorphic stereo vision system,
implemented over specialized hardware (FPGA - Field-Programmable Gate Array), which allows obtaining
reduced latencies on hardware implementation for stand-alone systems, and working in real time.Ministerio de Economía y Competitividad TEC2016-77785-PMinisterio de Economía y Competitividad TIN2016-80644-
Optimizing the energy consumption of spiking neural networks for neuromorphic applications
In the last few years, spiking neural networks have been demonstrated to
perform on par with regular convolutional neural networks. Several works have
proposed methods to convert a pre-trained CNN to a Spiking CNN without a
significant sacrifice of performance. We demonstrate first that
quantization-aware training of CNNs leads to better accuracy in SNNs. One of
the benefits of converting CNNs to spiking CNNs is to leverage the sparse
computation of SNNs and consequently perform equivalent computation at a lower
energy consumption. Here we propose an efficient optimization strategy to train
spiking networks at lower energy consumption, while maintaining similar
accuracy levels. We demonstrate results on the MNIST-DVS and CIFAR-10 datasets
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