440 research outputs found
Neuromorphic Approach Sensitivity Cell Modeling and FPGA Implementation
Neuromorphic engineering takes inspiration from biology to
solve engineering problems using the organizing principles of biological
neural computation. This field has demonstrated success in sensor based
applications (vision and audition) as well in cognition and actuators.
This paper is focused on mimicking an interesting functionality of the
retina that is computed by one type of Retinal Ganglion Cell (RGC).
It is the early detection of approaching (expanding) dark objects. This
paper presents the software and hardware logic FPGA implementation
of this approach sensitivity cell. It can be used in later cognition layers as
an attention mechanism. The input of this hardware modeled cell comes
from an asynchronous spiking Dynamic Vision Sensor, which leads to an
end-to-end event based processing system. The software model has been
developed in Java, and computed with an average processing time per
event of 370 ns on a NUC embedded computer. The output firing rate
for an approaching object depends on the cell parameters that represent
the needed number of input events to reach the firing threshold. For the
hardware implementation on a Spartan6 FPGA, the processing time is
reduced to 160 ns/event with the clock running at 50 MHz.Ministerio de EconomÃa y Competitividad TEC2016-77785-PUnión Europea FP7-ICT-60095
A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems
In this paper we present a methodological framework that meets novel
requirements emerging from upcoming types of accelerated and highly
configurable neuromorphic hardware systems. We describe in detail a device with
45 million programmable and dynamic synapses that is currently under
development, and we sketch the conceptual challenges that arise from taking
this platform into operation. More specifically, we aim at the establishment of
this neuromorphic system as a flexible and neuroscientifically valuable
modeling tool that can be used by non-hardware-experts. We consider various
functional aspects to be crucial for this purpose, and we introduce a
consistent workflow with detailed descriptions of all involved modules that
implement the suggested steps: The integration of the hardware interface into
the simulator-independent model description language PyNN; a fully automated
translation between the PyNN domain and appropriate hardware configurations; an
executable specification of the future neuromorphic system that can be
seamlessly integrated into this biology-to-hardware mapping process as a test
bench for all software layers and possible hardware design modifications; an
evaluation scheme that deploys models from a dedicated benchmark library,
compares the results generated by virtual or prototype hardware devices with
reference software simulations and analyzes the differences. The integration of
these components into one hardware-software workflow provides an ecosystem for
ongoing preparative studies that support the hardware design process and
represents the basis for the maturity of the model-to-hardware mapping
software. The functionality and flexibility of the latter is proven with a
variety of experimental results
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
Approaching Retinal Ganglion Cell Modeling and FPGA Implementation for Robotics
Taking inspiration from biology to solve engineering problems using the organizing
principles of biological neural computation is the aim of the field of neuromorphic engineering.
This field has demonstrated success in sensor based applications (vision and audition) as well as in
cognition and actuators. This paper is focused on mimicking the approaching detection functionality
of the retina that is computed by one type of Retinal Ganglion Cell (RGC) and its application to
robotics. These RGCs transmit action potentials when an expanding object is detected. In this work
we compare the software and hardware logic FPGA implementations of this approaching function
and the hardware latency when applied to robots, as an attention/reaction mechanism. The visual
input for these cells comes from an asynchronous event-driven Dynamic Vision Sensor, which leads
to an end-to-end event based processing system. The software model has been developed in Java,
and computed with an average processing time per event of 370 ns on a NUC embedded computer.
The output firing rate for an approaching object depends on the cell parameters that represent the
needed number of input events to reach the firing threshold. For the hardware implementation, on a
Spartan 6 FPGA, the processing time is reduced to 160 ns/event with the clock running at 50 MHz.
The entropy has been calculated to demonstrate that the system is not totally deterministic in response
to approaching objects because of several bioinspired characteristics. It has been measured that a
Summit XL mobile robot can react to an approaching object in 90 ms, which can be used as an
attentional mechanism. This is faster than similar event-based approaches in robotics and equivalent
to human reaction latencies to visual stimulus.Ministerio de EconomÃa y Competitividad TEC2016-77785-PComisión Europea FP7-ICT-60095
Simulation and implementation of novel deep learning hardware architectures for resource constrained devices
Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems
Interfacing of neuromorphic vision, auditory and olfactory sensors with digital neuromorphic circuits
The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems.
Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented in intelligent systems. The neuromorphic approach is widely applied in electronic sensing, particularly in vision, auditory, tactile and olfactory sensors. While conventional sensors generate a huge amount of redundant output data, neuromorphic sensors implement the biological concept of spike-based output to generate sparse output data that corresponds to a certain sensing event. The operation principle applied in these sensors supports reduced power consumption with operating efficiency comparable to conventional sensors. Although neuromorphic sensors such as Dynamic Vision Sensor (DVS), Dynamic and Active pixel Vision Sensor (DAVIS) and AEREAR2 are steadily expanding their scope of application in real-world systems, the lack of spike-based data processing algorithms and complex interfacing methods restricts its applications in low-cost standalone autonomous systems.
This research addresses the issue of interfacing between neuromorphic sensors and digital neuromorphic circuits. Current interfacing methods of these sensors are dependent on computers for output data processing. This approach restricts the portability of these sensors, limits their application in a standalone system and increases the overall cost of such systems. The proposed methodology simplifies the interfacing of these sensors with digital neuromorphic processors by utilizing AER communication protocols and neuromorphic hardware developed under the Convolution AER Vision Architecture for Real-time (CAVIAR) project. The proposed interface is simulated using a JAVA model that emulates a typical spikebased output of a neuromorphic sensor, in this case an olfactory sensor, and functions that process this data based on supervised learning. The successful implementation of this simulation suggests that the methodology is a practical solution and can be implemented in hardware. The JAVA simulation is compared to a similar model developed in Nengo, a standard large-scale neural simulation tool.
The successful completion of this research contributes towards expanding the scope of application of neuromorphic sensors in standalone intelligent systems. The easy interfacing method proposed in this thesis promotes the portability of these sensors by eliminating the dependency on computers for output data processing. The inclusion of neuromorphic Field Programmable Gate Array (FPGA) board allows reconfiguration and deployment of learning algorithms to implement adaptable systems. These low-power systems can be widely applied in biosecurity and environmental monitoring. With this thesis, we suggest directions for future research in neuromorphic standalone systems based on neuromorphic olfaction
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