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-

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

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

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

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-

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-

Production of 4-ethylphenol in alperujo by Lactobacillus pentosus

© 2014 Society of Chemical Industry. BACKGROUND: Alperujo is the paste generated from the two-phase extraction system of olive oil. This wet pomace must be stored for several months and, during this period, the formation of 4-ethyphenol provokes a strong off-odour. The aim of this work was to identify the microorganisms able to produce this volatile phenol. RESULTS: Yeast and bacterial strains were isolated from stored alperujo and tested for their ability to metabolize p-coumaric acid and form 4-ethylphenol. Among them, Lactobacillus pentosus was the microorganism that both in synthetic medium and alperujo gave rise to 4-ethylphenol formation. This microorganism did not grow in alperujo acidified to pH 2, thereby confirming that acidification as the best method to control odour emissions during alperujo storage. CONCLUSION: Lactic acid bacteria, particularly Lactobacillus pentosus, can be responsible for the formation of the off-odour caused by 4-ethylphenol during the storage of alperujo. This odour can be prevented by acidifying the alperujo.This research was supported by the Spanish Government and the European Union FEDER funds through the project AGL-2009-07512. The authors are grateful to Irene de la Rosa for technical help and Oleícola el Tejar SCA for providing the alperujo samples.Peer Reviewe

Fall-back time for photo-ionized Cs atoms attached to superfluid He-4 nanodroplets

We have studied the dynamic evolution of a Cs atom photo-excited from 6s to 6p and 7s states on a helium droplet using time-dependent 4He-DFT simulations. Depending on the excited electronic state, the Cs impurity remains on the droplet surface or it is ejected. Upon subsequent photo-ionization of the excited Cs atom the resulting Cs+ cation may either be ejected or come back to the droplet, depending on the time delay between photo-excitation and photo-ionization. We have calculated the critical time delay separating these two different behaviors, as well as final ion velocities. These observables will be used for future comparison with planned pump-probe experiments

Self-Assembled Lanthanide Antenna Glutathione Sensor for the Study of Immune Cells

This work was supported by grants CTQ2017-85658-R, BFU2015-67284-R, and PID2019-104366RB-C22 funded by MCIN/AEI/10.13039/501100011033/FEDER "Una manera de hacer Europa"; grant PID2020-114256RB-I00 funded by MCIN/AEI/10.13039/501100011033; grant A-FQM-386-UGR20 funded by FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades, and the CSIC grant 201580E073. Funding for open access charge: Universidad de Granada/CBUA.The small molecule 8-methoxy-2-oxo-1,2,4,5- tetrahydrocyclopenta[de]quinoline-3-carboxylic acid (2b) behaves as a reactive non-fluorescent Michael acceptor, which after reaction with thiols becomes fluorescent, and an efficient Eu3+ antenna, after self-assembling with this cation in water. This behavior makes 2b a highly selective GSH biosensor, which has demonstrated high potential for studies in murine and human cells of the immune system (CD4+ T, CD8+ T, and B cells) using flow cytometry. GSH can be monitored by the fluorescence of the product of addition to 2b (445 nm) or by the luminescence of Eu3+ (592 nm). 2b was able to capture baseline differences in GSH intracellular levels among murine and human CD4+ T, CD8+ T, and B cells. We also successfully used 2b to monitor intracellular changes in GSH associated with the metabolic variations governing the induction of CD4+ naiv̈ e T cells into regulatory T cells (TREG).MCIN/AEI/FEDER "Una manera de hacer Europa" CTQ2017-85658-R BFU2015-67284-R PID2019-104366RB-C22MCIN/AEI PID2020-114256RB-I00FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades A-FQM-386-UGR20CSIC grant 201580E073Universidad de Granada/CBU