64 research outputs found
Improved Contrast Sensitivity DVS and its Application to Event-Driven Stereo Vision
This paper presents a new DVS sensor with
one order of magnitude improved contrast sensitivity over
previous reported DVSs. This sensor has been applied to a
bio-inspired event-based binocular system that performs
3D event-driven reconstruction of a scene. Events from two
DVS sensors are matched by using precise timing
information of their ocurrence. To improve matching
reliability, satisfaction of epipolar geometry constraint is
required, and simultaneously available information on the
orientation is used as an additional matching constraint.Ministerio de Economía y Competitividad PRI-PIMCHI-2011-0768Ministerio de Economía y Competitividad TEC2009-10639-C04-01Junta de Andalucía TIC-609
Live Demonstration: Multiplexing AER Asynchronous Channels over LVDS Links with Flow-Control and Clock- Correction for Scalable Neuromorphic Systems
In this live demonstration we exploit the use of a
serial link for fast asynchronous communication in massively
parallel processing platforms connected to a DVS for realtime
implementation of bio-inspired vision processing on
spiking neural networks
MOSFET mismatch in weak/moderate inversion : model needs and implications for analog design
PostprintTrabajo presentado en ESSCIRC 2004. 29th European Solid-State Circuits Conference, Estoril, Portugal, 2003Based on mismatch measurements performed on very different CMOS technologies and large operating temperature range, we propose to model more adequately the mismatch in weak and moderate inversion by adding a new term related to the mismatch of the body effect factor dependence on the gate voltage. The model is introduced in a top-down analog design methodology, applied to the current mirror case, revealing some nonobvious design rules as well as typical misconceptions
Passive localization and detection of quadcopter UAVs by using Dynamic Vision Sensor
We present a new passive and low power
localization method for quadcopter UAVs (Unmanned aerial
vehicles) by using dynamic vision sensors. This method works
by detecting the speed of rotation of propellers that is normally
higher than the speed of movement of other objects in the
background. Dynamic vision sensors are fast and power
efficient. We have presented the algorithm along with the results
of implementation
On the use of orientation filters for 3D reconstruction in event-driven stereo vision
The recently developed Dynamic Vision Sensors (DVS) sense visual information asynchronously and code it into trains of events with sub-micro second temporal resolution. This high temporal precision makes the output of these sensors especially suited for dynamic 3D visual reconstruction, by matching corresponding events generated by two different sensors in a stereo setup. This paper explores the use of Gabor filters to extract information about the orientation of the object edges that produce the events, therefore increasing the number of constraints applied to the matching algorithm. This strategy provides more reliably matched pairs of events, improving the final 3D reconstruction.ERANET PRI-PIMCHI- 2011-0768Ministerio de Economía y Competitividad TEC2009-10639-C04-01, TEC2012-37868- C04-01Junta de Andalucía TIC-609
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
Convolutional neural networks (CNNs) have become the dominant neural network
architecture for solving many state-of-the-art (SOA) visual processing tasks.
Even though Graphical Processing Units (GPUs) are most often used in training
and deploying CNNs, their power efficiency is less than 10 GOp/s/W for
single-frame runtime inference. We propose a flexible and efficient CNN
accelerator architecture called NullHop that implements SOA CNNs useful for
low-power and low-latency application scenarios. NullHop exploits the sparsity
of neuron activations in CNNs to accelerate the computation and reduce memory
requirements. The flexible architecture allows high utilization of available
computing resources across kernel sizes ranging from 1x1 to 7x7. NullHop can
process up to 128 input and 128 output feature maps per layer in a single pass.
We implemented the proposed architecture on a Xilinx Zynq FPGA platform and
present results showing how our implementation reduces external memory
transfers and compute time in five different CNNs ranging from small ones up to
the widely known large VGG16 and VGG19 CNNs. Post-synthesis simulations using
Mentor Modelsim in a 28nm process with a clock frequency of 500 MHz show that
the VGG19 network achieves over 450 GOp/s. By exploiting sparsity, NullHop
achieves an efficiency of 368%, maintains over 98% utilization of the MAC
units, and achieves a power efficiency of over 3TOp/s/W in a core area of
6.3mm. As further proof of NullHop's usability, we interfaced its FPGA
implementation with a neuromorphic event camera for real time interactive
demonstrations
Event-driven stereo vision with orientation filters
The recently developed Dynamic Vision Sensors
(DVS) sense dynamic visual information asynchronously and
code it into trains of events with sub-micro second temporal
resolution. This high temporal precision makes the output of
these sensors especially suited for dynamic 3D visual
reconstruction, by matching corresponding events generated by
two different sensors in a stereo setup. This paper explores the
use of Gabor filters to extract information about the orientation
of the object edges that produce the events, applying the
matching algorithm to the events generated by the Gabor filters
and not to those produced by the DVS. This strategy provides
more reliably matched pairs of events, improving the final 3D
reconstruction.European Union PRI-PIMCHI-2011-0768Ministerio de Economía y Competitividad TEC2009-10639-C04-01Ministerio de Economía y Competitividad TEC2012-37868-C04-01Junta de Andalucía TIC-609
Implementation of binary stochastic STDP learning using chalcogenide-based memristive devices
The emergence of nano-scale memristive devices encouraged many different
research areas to exploit their use in multiple applications. One of the
proposed applications was to implement synaptic connections in bio-inspired
neuromorphic systems. Large-scale neuromorphic hardware platforms are being
developed with increasing number of neurons and synapses, having a critical
bottleneck in the online learning capabilities. Spike-timing-dependent
plasticity (STDP) is a widely used learning mechanism inspired by biology which
updates the synaptic weight as a function of the temporal correlation between
pre- and post-synaptic spikes. In this work, we demonstrate experimentally that
binary stochastic STDP learning can be obtained from a memristor when the
appropriate pulses are applied at both sides of the device
Performance Comparison of Time-Step-Driven versus Event-Driven Neural State Update Approaches in SpiNNaker
The SpiNNaker chip is a multi-core processor optimized for neuromorphic applications. Many SpiNNaker chips are assembled to make a highly parallel million core platform. This system can be used for simulation of a large number of neurons in real-time. SpiNNaker is using a general purpose ARM processor that gives a high amount of flexibility to implement different methods for processing spikes. Various libraries and packages are provided to translate a high-level description of Spiking Neural Networks (SNN) to low-level machine language that can be used in the ARM processors. In this paper, we introduce and compare three different methods to implement this intermediate layer of abstraction. We have examined the advantages of each method by various criteria, which can be useful for professional users to choose between them. All the codes that are used in this paper are available for academic propose.EU H2020 grant 644096 ECOMODEEU H2020 grant 687299 NEURAM3Ministry of Economy and Competitivity (Spain) / European Regional Development Fund TEC2015-63884-C2-1-P (COGNET
A Real-Time, Event Driven Neuromorphic System for Goal-Directed Attentional Selection
Computation with spiking neurons takes advantage of the
abstraction of action potentials into streams of stereotypical events, which
encode information through their timing. This approach both reduces
power consumption and alleviates communication bottlenecks. A number
of such spiking custom mixed-signal address event representation
(AER) chips have been developed in recent years.
In this paper, we present i) a flexible event-driven platform consisting
of the integration of a visual AER sensor and the SpiNNaker system,
a programmable massively parallel digital architecture oriented to the
simulation of spiking neural networks; ii) the implementation of a neural
network for feature-based attentional selection on this platfor
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