6,521 research outputs found
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
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
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
A FPGA Spike-Based Robot Controlled with Neuro-inspired VITE
This paper presents a spike-based control system applied to a fixed
robotic platform. Our aim is to take a step forward to a future complete spikes
processing architecture, from vision to direct motor actuation. This paper covers
the processing and actuation layer over an anthropomorphic robot. In this way,
the processing layer uses the neuro-inspired VITE algorithm, for reaching a target,
based on PFM taking advantage of spike system information: its frequency.
Thus, all the blocks of the system are based on spikes. Each layer is implemented
within a FPGA board and spikes communication is codified under the
AER protocol. The results show an accurate behavior of the robotic platform
with 6-bit resolution for a 130º range per joint, and an automatic speed control
of the algorithm. Up to 96 motor controllers could be integrated in the same
FPGA, allowing the positioning and object grasping by more complex anthropomorphic
robots.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Ministerio de Economía y Competitividad TEC2012-37868-C04-0
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
A Binaural Neuromorphic Auditory Sensor for FPGA: A Spike Signal Processing Approach
This paper presents a new architecture, design
flow, and field-programmable gate array (FPGA) implementation
analysis of a neuromorphic binaural auditory sensor, designed
completely in the spike domain. Unlike digital cochleae that
decompose audio signals using classical digital signal processing
techniques, the model presented in this paper processes information
directly encoded as spikes using pulse frequency modulation
and provides a set of frequency-decomposed audio information
using an address-event representation interface. In this case,
a systematic approach to design led to a generic process for
building, tuning, and implementing audio frequency decomposers
with different features, facilitating synthesis with custom features.
This allows researchers to implement their own parameterized
neuromorphic auditory systems in a low-cost FPGA in order to
study the audio processing and learning activity that takes place
in the brain. In this paper, we present a 64-channel binaural
neuromorphic auditory system implemented in a Virtex-5 FPGA
using a commercial development board. The system was excited
with a diverse set of audio signals in order to analyze its response
and characterize its features. The neuromorphic auditory system
response times and frequencies are reported. The experimental
results of the proposed system implementation with 64-channel
stereo are: a frequency range between 9.6 Hz and 14.6 kHz
(adjustable), a maximum output event rate of 2.19 Mevents/s,
a power consumption of 29.7 mW, the slices requirements
of 11 141, and a system clock frequency of 27 MHz.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA
This paper presents a statistical study on a neuro-inspired spike-based implementation of the Vector-Integration-To-End-Point motor controller (SVITE) and compares its deterministic neuron-model stream of spikes with a proposed modification that converts the model, and thus the controller, in a Poisson like spike stream distribution. A set of hardware pseudo-random numbers generators, based on a Linear Feedback Shift Register (LFSR), have been introduced in the neuron-model so that they reach a closer biological neuron behavior. To validate the new neuron-model behavior a comparison between the Inter-Spikes-Interval empirical data and the Exponential and Gamma distributions has been carried out using the Kolmogorov–Smirnoff test. An in-hardware validation of the controller has been performed in a Spartan6 FPGA to drive directly with spikes DC motors from robotics to study the behavior and viability of the modified controller with random components.
The results show that the original deterministic spikes distribution of the controller blocks can be swapped with Poisson distributions using 30-bit LFSRs. The comparative between the usable controlling signals such as the trajectory and the speed profile using a deterministic and the new controller show a standard deviation of 11.53 spikes/s and 3.86 spikes/s respectively. These rates do not affect our system because, within Pulse Frequency Modulation, in order to drive the motors, time length can be fixed to spread the spikes. Tuning this value, the slow rates could be filtered by the motor. Therefore, this SVITE neuro-inspired controller can be integrated within complex neuromorphic architectures with Poisson-like neurons
Analog VLSI-Based Modeling of the Primate Oculomotor System
One way to understand a neurobiological system is by building a simulacrum that replicates its behavior in real time using similar constraints. Analog very large-scale integrated (VLSI) electronic circuit technology provides such an enabling technology. We here describe a neuromorphic system that is part of a long-term effort to understand the primate oculomotor system. It requires both fast sensory processing and fast motor control to interact with the world. A one-dimensional hardware model of the primate eye has been built that simulates the physical dynamics of the biological system. It is driven by two different analog VLSI chips, one mimicking cortical visual processing for target selection and tracking and another modeling brain stem circuits that drive the eye muscles. Our oculomotor plant demonstrates both smooth pursuit movements, driven by a retinal velocity error signal, and saccadic eye movements, controlled by retinal position error, and can reproduce several behavioral, stimulation, lesion, and adaptation experiments performed on primates
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