1,056 research outputs found
On the Designing of Spikes Band-Pass Filters for FPGA
In this paper we present two implementations of spike-based bandpass
filters, which are able to reject out-of-band frequency components in the
spike domain. First one is based on the use of previously designed spike-based
low-pass filters. With this architecture the quality factor, Q, is lower than 0.5.
The second implementation is inspired in the analog multi-feedback filters
(MFB) topology, it provides a higher than 1 Q factor, and ideally tends to
infinite. These filters have been written in VHLD, and synthesized for FPGA.
Two spike-based band-pass filters presented take advantages of the spike rate
coded representation to perform a massively parallel processing without complex
hardware units, like floating point arithmetic units, or a large memory. These low
requirements of hardware allow the integration of a high number of filters inside
a FPGA, allowing to process several spike coded signals fully in parallel.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
Interfacing PDM sensors with PFM spiking systems: application for Neuromorphic Auditory Sensors
In this paper we present a sub-system to convert
audio information from low-power MEMS microphones with
pulse density modulation (PDM) output into rate coded spike
streams. These spikes represent the input signal of a Neuromorphic
Auditory Sensor (NAS), which is implemented with Spike
Signal Processing (SSP) building blocks. For this conversion, we
have designed a HDL component for FPGA able to interface
with PDM microphones and converts their pulses to temporal
distributed spikes following a pulse frequency modulation (PFM)
scheme with an accurate configurable Inter-Spike-Interval. The
new FPGA component has been tested in two scenarios, first as a
stand-alone circuit for its characterization, and then it has been
integrated with a full NAS design to verify its behavior. This
PDM interface demands less than 1% of a Spartan 6 FPGA
resources and has a power consumption below 5mW.Ministerio de Economía y Competitividad TEC2016-77785-
Building Blocks for Spikes Signals Processing
Neuromorphic engineers study models and
implementations of systems that mimic neurons behavior in the
brain. Neuro-inspired systems commonly use spikes to
represent information. This representation has several
advantages: its robustness to noise thanks to repetition, its
continuous and analog information representation using digital
pulses, its capacity of pre-processing during transmission time,
... , Furthermore, spikes is an efficient way, found by nature, to
codify, transmit and process information. In this paper we
propose, design, and analyze neuro-inspired building blocks
that can perform spike-based analog filters used in signal
processing. We present a VHDL implementation for FPGA.
Presented building blocks take advantages of the spike rate
coded representation to perform a massively parallel processing
without complex hardware units, like floating point arithmetic
units, or a large memory. Those low requirements of hardware
allow the integration of a high number of blocks inside a FPGA,
allowing to process fully in parallel several spikes coded signals.Junta de Andalucía P06-TIC-O1417Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Ministerio de Ciencia e Innovación TEC2006-11730-C03-0
Musical notes classification with Neuromorphic Auditory System using FPGA and a Convolutional Spiking Network
In this paper, we explore the capabilities of a sound
classification system that combines both a novel FPGA cochlear
model implementation and a bio-inspired technique based on a
trained convolutional spiking network. The neuromorphic
auditory system that is used in this work produces a form of
representation that is analogous to the spike outputs of the
biological cochlea. The auditory system has been developed using
a set of spike-based processing building blocks in the frequency
domain. They form a set of band pass filters in the spike-domain
that splits the audio information in 128 frequency channels, 64
for each of two audio sources. Address Event Representation
(AER) is used to communicate the auditory system with the
convolutional spiking network. A layer of convolutional spiking
network is developed and trained on a computer with the ability
to detect two kinds of sound: artificial pure tones in the presence
of white noise and electronic musical notes. After the training
process, the presented system is able to distinguish the different
sounds in real-time, even in the presence of white noise.Ministerio de Economía y Competitividad TEC2012-37868-C04-0
Real-time motor rotation frequency detection with event-based visual and spike-based auditory AER sensory integration for FPGA
Multisensory integration is commonly
used in various robotic areas to collect more
environmental information using different and
complementary types of sensors. Neuromorphic
engineers mimics biological systems behavior to
improve systems performance in solving engineering
problems with low power consumption. This work
presents a neuromorphic sensory integration scenario
for measuring the rotation frequency of a motor using
an AER DVS128 retina chip (Dynamic Vision Sensor)
and a stereo auditory system on a FPGA completely
event-based. Both of them transmit information with
Address-Event-Representation (AER). This
integration system uses a new AER monitor hardware
interface, based on a Spartan-6 FPGA that allows two
operational modes: real-time (up to 5 Mevps through
USB2.0) and data logger mode (up to 20Mevps for
33.5Mev stored in onboard DDR RAM). The sensory
integration allows reducing prediction error of the
rotation speed of the motor since audio processing
offers a concrete range of rpm, while DVS can be
much more accurate.Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0
Simulating Building Blocks for Spikes Signals Processing
In this paper we will explain in depth how we have used Simulink
with the addition of Xilinx System Generation to design a simulation
framework for testing and analyzing neuro-inspired elements for spikes rate
coded signals processing. Those elements have been designed as building
blocks, which represent spikes processing primitives, combining them we have
designed more complex blocks, which behaves like analog frequency filter
using digital circuits. This kind of computation performs a massively parallel
processing without complex hardware units. Spikes processing building blocks
have been written in VHDL to be implemented for FPGA. Xilinx System
Generator allows co-simulating VHDL entities together with Simulink
components, providing an easy interface for presented building block
simulations and analysis.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
Multilayer Spiking Neural Network for Audio Samples Classification Using SpiNNaker
Audio classification has always been an interesting subject of research
inside the neuromorphic engineering field. Tools like Nengo or Brian, and hardware
platforms like the SpiNNaker board are rapidly increasing in popularity in
the neuromorphic community due to the ease of modelling spiking neural
networks with them. In this manuscript a multilayer spiking neural network for
audio samples classification using SpiNNaker is presented. The network consists
of different leaky integrate-and-fire neuron layers. The connections between them
are trained using novel firing rate based algorithms and tested using sets of pure
tones with frequencies that range from 130.813 to 1396.91 Hz. The hit rate
percentage values are obtained after adding a random noise signal to the original
pure tone signal. The results show very good classification results (above 85 %
hit rate) for each class when the Signal-to-noise ratio is above 3 decibels, validating
the robustness of the network configuration and the training step.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors
Auscultation is one of the most used techniques for
detecting cardiovascular diseases, which is one of the main causes
of death in the world. Heart murmurs are the most common abnormal
finding when a patient visits the physician for auscultation.
These heart sounds can either be innocent, which are harmless, or
abnormal, which may be a sign of a more serious heart condition.
However, the accuracy rate of primary care physicians and expert
cardiologists when auscultating is not good enough to avoid most
of both type-I (healthy patients are sent for echocardiogram) and
type-II (pathological patients are sent home without medication or
treatment) errors made. In this paper, the authors present a novel
convolutional neural network based tool for classifying between
healthy people and pathological patients using a neuromorphic
auditory sensor for FPGA that is able to decompose the audio into
frequency bands in real time. For this purpose, different networks
have been trained with the heart murmur information contained in
heart sound recordings obtained from nine different heart sound
databases sourced from multiple research groups. These samples
are segmented and preprocessed using the neuromorphic auditory
sensor to decompose their audio information into frequency
bands and, after that, sonogram images with the same size are
generated. These images have been used to train and test different
convolutional neural network architectures. The best results
have been obtained with a modified version of the AlexNet model,
achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%,
PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid
cardiologists and primary care physicians in the auscultation process,
improving the decision making task and reducing type-I and
type-II errors.Ministerio de Economía y Competitividad TEC2016-77785-
ED-Scorbot: A Robotic test-bed Framework for FPGA-based Neuromorphic systems
Neuromorphic engineering is a growing and
promising discipline nowadays. Neuro-inspiration and
brain understanding applied to solve engineering
problems is boosting new architectures, solutions and
products today. The biological brain and neural systems
process information at relatively low speeds through
small components, called neurons, and it is impressive how
they connect each other to construct complex
architectures to solve in a quasi-instantaneous way
visual and audio processing tasks, object detection and
tracking, target approximation, grasping…, etc., with very
low power. Neuromorphs are beginning to be very promising
for a new era in the development of new sensors,
processors, robots and software systems that mimic
these biological systems. The event-driven Scorbot (EDScorbot)
is a robotic arm plus a set of FPGA / microcontroller’s
boards and a library of FPGA logic joined in a completely
event-based framework (spike-based) from the sensors to the
actuators. It is located in Seville (University of Seville) and
can be used remotely. Spike-based commands, through
neuro-inspired motor controllers, can be sent to the
robot after visual processing object detection and
tracking for grasping or manipulation, after complex
visual and audio-visual sensory fusion, or after performing
a learning task. Thanks to the cascade FPGA
architecture through the Address-Event-Representation
(AER) bus, supported by specialized boards, resources for
algorithms implementation are not limited.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
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