Development of an interface for digital neuromorphic hardware based on an FPGA

Exploring and understanding the functioning of the human brain is one of the greatest challenges for current research. Neuromorphic engineering tries to address this challenge by abstracting biological mechanisms and translating them into technology. Via the abstraction process and experiments with the resulting technical system, an attempt is made to obtain information about the biological counterpart. One subsection of Neuromorphic Engineering (NE) are Spiking Neural Networks (SNN), which describe the structures of the human brain more and more closely than Artificial Neural Networks (ANN). Together with their dedicated hardware, SNNs provide a good platform for developing new algorithms for information processing. In the context of these neuromorphic hardware platforms, this paper aims to develop an interface for a digital hardware platform (SPINN-3 Development Board) to enable the use of industrial or conventional sensors and thus create new approaches for experimental research. The basis for this endeavor is a Field Programmable Gate Array (FPGA), which is placed as a gateway between the sensors and the neuromorphic hardware. Overall, the developed system provides a robust solution for a wide variety of investigations related to neuromorphic hardware and SNNs. Furthermore, the solution also offers suitable possibilities to monitor all processes within the system in order to obtain suitable measurements, which can be examined in search of meaningful results.Comment: Accepted for publication with Proceedings of the Unified Conference of DAMAS, InCoME and TEPEN Conferences (UNIfied 2023), Springer Natur

ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware

Neuromorphic perception with event-based sensors, asynchronous hardware and spiking neurons is showing promising results for real-time and energy-efficient inference in embedded systems. The next promise of brain-inspired computing is to enable adaptation to changes at the edge with online learning. However, the parallel and distributed architectures of neuromorphic hardware based on co-localized compute and memory imposes locality constraints to the on-chip learning rules. We propose in this work the Event-based Three-factor Local Plasticity (ETLP) rule that uses (1) the pre-synaptic spike trace, (2) the post-synaptic membrane voltage and (3) a third factor in the form of projected labels with no error calculation, that also serve as update triggers. We apply ETLP with feedforward and recurrent spiking neural networks on visual and auditory event-based pattern recognition, and compare it to Back-Propagation Through Time (BPTT) and eProp. We show a competitive performance in accuracy with a clear advantage in the computational complexity for ETLP. We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learn spatio-temporal patterns with a rich temporal structure. Finally, we provide a proof of concept hardware implementation of ETLP on FPGA to highlight the simplicity of its computational primitives and how they can be mapped into neuromorphic hardware for online learning with low-energy consumption and real-time interaction

Real-time detection of uncalibrated sensors using Neural Networks

Nowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use. However, the retrieved information must be reliable. Anomalies in the behavior of sensors can give rise to critical consequences such as ruining a scientific project or jeopardizing the quality of the production in industrial production lines. One of the more subtle kind of anomalies are uncalibrations. An uncalibration is said to take place when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. This solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. Then, after trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively. This solution can be adapted to different contexts by means of transfer learning, whose application allows for the addition of new sensors, the deployment into new environments and the retraining of the model with minimum amounts of data

Low-Cost Throttle-By-Wire-System Architecture For Two-Wheeler Vehicles

This paper investigates the performance of a low-cost Throttle-by-Wire-System (TbWS) for two-wheeler applications. Its consisting of an AMR throttle position sensor and a position controlled stepper motor driven throttle valve actuator. The decentralized throttle position sensor is operating contactless and acquires redundant data. Throttle valve actuation is realized through a position controlled stepper motor, sensing its position feedback by Hall effect. Using a PI-controller the stepper motors position is precisely set. Sensor and actuator units are transceiving data by a CAN bus. Furthermore, failsafe functions, plausibility checks, calibration algorithms and energy saving modes have been implemented. Both modules have been evaluated within a Hardware-in-the-Loop test environment in terms of reliability and measurement/positioning performance before the TbWS was integrated in a Peugeot Kisbee 50 4T (Euro 5/injected). Finally, the sensor unit comes with a measurement deviation of less then 0.16% whereas the actuator unit can approach throttle valve positions with a deviation of less then 0.37%. The actuators settling time does not exceed 0.13s while stable, step-loss free and noiseless operation

WaLiN-GUI: a graphical and auditory tool for neuron-based encoding

Neuromorphic computing relies on spike-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spiking representation. This is usually accomplished using the real valued data as current input to a spiking neuron model, and tuning the neuron's parameters to match a desired, often biologically inspired behaviour. We developed a tool, the WaLiN-GUI, that supports the investigation of neuron models and parameter combinations to identify suitable configurations for neuron-based encoding of sample-based data into spike trains. Due to the generalized LIF model implemented by default, next to the LIF and Izhikevich neuron models, many spiking behaviors can be investigated out of the box, thus offering the possibility of tuning biologically plausible responses to the input data. The GUI is provided open source and with documentation, being easy to extend with further neuron models and personalize with data analysis functions.Comment: 4 pages, 1 figur

Implementation of Processing Functions for Autonomous Power Quality Measurement Equipment: A Performance Evaluation of CPU and FPGA-Based Embedded System

Motivated by the effects of deregulation over power quality and the subsequent need of new types of measurements, this paper assesses different implementations of an estimate for the spectral kurtosis, considered as a low-level harmonic detection. Performance of a processor-based system is compared with a field programmable gate array (FPGA)-based solution, in order to evaluate the accuracy of this processing function for implementation in autonomous measurement equipment. The fourth-order spectrum, with applications in different fields, needs advanced digital signal processing, making it necessary to compare implementation alternatives. In order to obtain reproducible results, the implementations have been developed using common design and programming tools. Several characteristics of the implementations are compared, showing that the increasing complexity and reduced cost of the current FPGA models make the implementation of complex mathematical functions feasible. We show that FPGAs improve the processing capability of the best processor using an operating frequency 33 times lower. This fact strongly supports its implementation in hand-held instruments

A two-phase genetic algorithm to model the menisca horn repaired with suture

Menisci suturing is a common surgical technique nowadays. Menisci have been modeled with different degrees of complexity in finite element models (FEM) of the human knee, but there are few works focused on simulating the meniscus subjected to traction loads in its longitudinal direction, such as those produced by sutures after repair. Moreover, there are no models that include the effect of the orifice for the suture. This study develops a material model of the meniscal horn when it is pulled by the thread used to reattach its root.Universidad de Málaga.Campus de Excelencia Internacional Andalucía Tech. Subvencionado por el Proyecto del Plan Nacional RTI2018-094339-B-I00 y por el Proyecto de la Junta de Andalucía P20-00294

Estudio preliminar para la caracterización del tejido de la raíz meniscal humana reparada con sutura transtibial

Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.Los meniscos son estructuras fibrocartilaginosas gue aumentan la congruencia entre los dos cartílagos articulares, de manera que la transmisión de la carga se produce en una zona de contacto más amplia y el nivel de presiones en las superficies se reduce. Los meniscos se anclan directamente al hueso de la tibia mediante cuatro raíces, junto con otras uniones ligamentosas que tienen una contribución menor. Estas raíces son los principales restrictores de la extrusión meniscal pero, al mismo tiempo, deben permitir cierta movilidad para adaptar la posición del menisco durante la movilidad de la rodilla. Cuando se produce una avulsión de las raíces meniscales se recurre a su reinserción en intervenciones quirúrgicas que implican el suturado en la zona del cuerno meniscal que, por tanto, debe ser horadado para el paso de la sutura. A partir de los resultados y las conclusiones obtenidas en los trabajos previos del Laboratorio de Biomecánica Clínica de Andalucía, se propone profundizar en la caracterización del tejido de la raíz meniscal del conjunto menisco-sutura y obtener un modelo de su comportamiento que permita analizar la respuesta del tejido en esa zona ante diferentes materiales, cargas o posiciones del punto de inserción. Se presenta en este trabajo el algoritmo desarrollado para la definición de un modelo de material en el cuerno meniscal suturado, adjuntando resultados iniciales de su aplicación.El trabajo se ha realizado con financiación otorgada por el Campus de Excelencia Internacional Andalucía Tech de la Universidad de Málaga y MCIU/AEI/FEDER, UE (ref. RTI2018-094339-B-I00)