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

Live Demonstration: Real-time motor rotation frequency detection by spike-based visual and auditory AER sensory integration for FPGA

Multisensory integration is commonly used in various robotic areas to collect much more information from an environment using different and complementary types of sensors. This demonstration presents a scenario where the motor rotation frequency is obtained using an AER DVS128 retina chip (Dynamic Vision Sensor) and a frequency decomposer auditory system on a FPGA that mimics a biological cochlea. Both of them are spike-based sensors with Address-Event-Representation (AER) outputs. A new AER monitor hardware interface, based on a Spartan-6 FPGA, allows two operational modes: real-time (up to 5 Mevps through USB2.0) and off-line mode (up to 20Mevps and 33.5Mev stored in DDR RAM). The sensory integration allows the bio-inspired cochlea limit to provide a concrete range of rpm approaches, which are obtained by the silicon retina.Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0

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

Longitudinal study of low and high achievers in early mathematics

Background. Longitudinal studies allow us to identify, which specific maths skills are weak in young children, and whether there is a continuing weakness in these areas throughout their school years. Aims. This 2-year study investigated whether certain socio-demographic variables affect early mathematical competency in children aged 5–7 years. Sample. A randomly selected sample of 127 students (64 female; 63 male) participated. At the start of the study, the students were approximately 5 years old (M = 5.2; SD = 0.28; range = 4.5–5.8). Method. The students were assessed using the Early Numeracy Test and then allocated to a high (n = 26), middle (n = 76), or low (n = 25) achievers group. The same children were assessed again with the Early Numeracy Test at 6 and 7 years old, respectively. Eight socio-demographic characteristics were also evaluated: family model, education of the parent(s), job of the parent(s), number of family members, birth order, number of computers at home, frequency of teacher visits, and hours watching television. Results. Early Numeracy Test scores were more consistent for the high-achievers group than for the low-achievers group. Approximately 5.5% of low achievers obtained low scores throughout the study. A link between specific socio-demographic characteristics and early achievement in mathematics was only found for number of computers at home. Conclusions. The level of mathematical ability among students aged 5–7 years remains relatively stable regardless of the initial level of achievement. However, early screening for mathematics learning disabilities could be useful in helping low-achieving students overcome learning obstacles.This material is based on work supported by the Spanish Ministry of Science & Technology grant no. SEJ2007-62420/EDUC and Junta de Andalucia grant no. P09-HUM-4918

A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker

The study and monitoring of the behavior of wildlife has always been a subject of great interest. Although many systems can track animal positions using GPS systems, the behavior classification is not a common task. For this work, a multi-sensory wearable device has been designed and implemented to be used in the Doñana National Park in order to control and monitor wild and semiwild life animals. The data obtained with these sensors is processed using a Spiking Neural Network (SNN), with Address-Event-Representation (AER) coding, and it is classified between some fixed activity behaviors. This works presents the full infrastructure deployed in Doñana to collect the data, the wearable device, the SNN implementation in SpiNNaker and the classification results.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

Performance evaluation over HW/SW co-design SoC memory transfers for a CNN accelerator

Many FPGAs vendors have recently included embedded processors in their devices, like Xilinx with ARM-Cortex A cores, together with programmable logic cells. These devices are known as Programmable System on Chip (PSoC). Their ARM cores (embedded in the processing system or PS) communicates with the programmable logic cells (PL) using ARM-standard AXI buses. In this paper we analyses the performance of exhaustive data transfers between PS and PL for a Xilinx Zynq FPGA in a co-design real scenario for Convolutional Neural Networks (CNN) accelerator, which processes, in dedicated hardware, a stream of visual information from a neuromorphic visual sensor for classification. In the PS side, a Linux operating system is running, which recollects visual events from the neuromorphic sensor into a normalized frame, and then it transfers these frames to the accelerator of multi-layered CNNs, and read results, using an AXI-DMA bus in a per-layer way. As these kind of accelerators try to process information as quick as possible, data bandwidth becomes critical and maintaining a good balanced data throughput rate requires some considerations. We present and evaluate several data partitioning techniques to improve the balance between RX and TX transfer and two different ways of transfers management: through a polling routine at the userlevel of the OS, and through a dedicated interrupt-based kernellevel driver. We demonstrate that for longer enough packets, the kernel-level driver solution gets better timing in computing a CNN classification example. Main advantage of using kernel-level driver is to have safer solutions and to have tasks scheduling in the OS to manage other important processes for our application, like frames collection from sensors and their normalization.Ministerio de Economía y Competitividad TEC2016-77785-

Preliminary hydrogeological characterization of an evaporite karst area (province of Cordoba, South Spain)

The northern sector of the Subbetic Domain in the Betic Cordillera is formed by an olistostrome unit known as the Chaotic Subbetic Complex (CSC). This megabreccia is basically made of Triassic (Keuper) clays and evaporites (gypsum, anhidrite and halite) as well as blocks of other lithologies (limestones, dolostones, sandstones, etc). Despite that low permeability has been traditionally assumed for these materials, water flow and storage through them is likely derived of their aquitard behavior, but also because of the highly permeable conduits generated by dissolution/karstification processes within the evaporite rocks. The geological complexity of the CSC materials determines their hydrogeological heterogeneity, with groundwater flow systems of different length and various scales from recharge areas to discharge zones. Three springs draining the CSC outcrops have been identified around an evaporitic karst plateau located between the Anzur River (to the North) and the Genil River (to the South), in the province of Cordoba (Spain). Data logger devices have been installed in two of them, located at the Anzur River (left margin), providing an hourly record of discharge, electrical conductivity and water temperature. Water samples have been collected fortnightly for subsequent chemical analysis. After two years of record, the results obtained show that the response of the springs to rainfall events is completely different between them. One has a clearly karstic behavior, with a rapid response to recharge whereas the other one is more inertial, and variations in its waters occur in a yearly scale. This is an evidence of the aforementioned hydrogeological heterogeneity of the CSC.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Event-based Row-by-Row Multi-convolution engine for Dynamic-Vision Feature Extraction on FPGA

Neural networks algorithms are commonly used to recognize patterns from different data sources such as audio or vision. In image recognition, Convolutional Neural Networks are one of the most effective techniques due to the high accuracy they achieve. This kind of algorithms require billions of addition and multiplication operations over all pixels of an image. However, it is possible to reduce the number of operations using other computer vision techniques rather than frame-based ones, e.g. neuromorphic frame-free techniques. There exists many neuromorphic vision sensors that detect pixels that have changed their luminosity. In this study, an event-based convolution engine for FPGA is presented. This engine models an array of leaky integrate and fire neurons. It is able to apply different kernel sizes, from 1x1 to 7x7, which are computed row by row, with a maximum number of 64 different convolution kernels. The design presented is able to process 64 feature maps of 7x7 with a latency of 8.98 s.Ministerio de Economía y Competitividad TEC2016-77785-

Accuracy Improvement of Neural Networks Through Self-Organizing-Maps over Training Datasets

Although it is not a novel topic, pattern recognition has become very popular and relevant in the last years. Different classification systems like neural networks, support vector machines or even complex statistical methods have been used for this purpose. Several works have used these systems to classify animal behavior, mainly in an offline way. Their main problem is usually the data pre-processing step, because the better input data are, the higher may be the accuracy of the classification system. In previous papers by the authors an embedded implementation of a neural network was deployed on a portable device that was placed on animals. This approach allows the classification to be done online and in real time. This is one of the aims of the research project MINERVA, which is focused on monitoring wildlife in Do˜nana National Park using low power devices. Many difficulties were faced when pre-processing methods quality needed to be evaluated. In this work, a novel pre-processing evaluation system based on self-organizing maps (SOM) to measure the quality of the neural network training dataset is presented. The paper is focused on a three different horse gaits classification study. Preliminary results show that a better SOM output map matches with the embedded ANN classification hit improvement.Junta de Andalucía P12-TIC-1300Ministerio de Economía y Competitividad TEC2016-77785-

Embedded neural network for real-time animal behavior classification

Recent biological studies have focused on understanding animal interactions and welfare. To help biolo- gists to obtain animals’ behavior information, resources like wireless sensor networks are needed. More- over, large amounts of obtained data have to be processed off-line in order to classify different behaviors.There are recent research projects focused on designing monitoring systems capable of measuring someanimals’ parameters in order to recognize and monitor their gaits or behaviors. However, network unre- liability and high power consumption have limited their applicability.In this work, we present an animal behavior recognition, classification and monitoring system based ona wireless sensor network and a smart collar device, provided with inertial sensors and an embeddedmulti-layer perceptron-based feed-forward neural network, to classify the different gaits or behaviorsbased on the collected information. In similar works, classification mechanisms are implemented in aserver (or base station). The main novelty of this work is the full implementation of a reconfigurableneural network embedded into the animal’s collar, which allows a real-time behavior classification andenables its local storage in SD memory. Moreover, this approach reduces the amount of data transmittedto the base station (and its periodicity), achieving a significantly improving battery life. The system hasbeen simulated and tested in a real scenario for three different horse gaits, using different heuristics andsensors to improve the accuracy of behavior recognition, achieving a maximum of 81%.Junta de Andalucía P12-TIC-130