Mejora de computación neuromórfica con arquitecturas avanzadas de redes neuronales por impulsos

La computación neuromórfica (NC, del inglés neuromorphic computing) pretende revolucionar el campo de la inteligencia artificial. Implica diseñar e implementar sistemas electrónicos que simulen el comportamiento de las neuronas biológicas utilizando hardware especializado, como matrices de puertas programables en campo (FPGA, del ingl´es field-programmable gate array) o chips neuromórficos dedicados [1, 2]. NC está diseñado para ser altamente eficiente, optimizado para bajo consumo de energía y alto paralelismo [3]. Estos sistemas son adaptables a entornos cambiantes y pueden aprender durante la operación, lo que los hace muy adecuados para resolver problemas dinámicos e impredecibles [4]. Sin embargo, el uso de NC para resolver problemas de la vida real actualmente está limitado porque el rendimiento de las redes neuronales por impulsos (SNN), las redes neuronales empleadas en NC, no es tan alta como el de los sistemas de computación tradicionales, como los alcanzados en dispositivos de aprendizaje profundo especializado, en términos de precisión y velocidad de aprendizaje [5, 6]. Varias razones contribuyen a la brecha de rendimiento: los SNN son más difíciles de entrenar debido a que necesitan algoritmos de entrenamiento especializados [7, 8]; son más sensibles a hiperparámetros, ya que son sistemas dinámicos con interacciones complejas [9], requieren conjuntos de datos especializados (datos neuromórficos) que actualmente son escasos y de tamaño limitado [10], y el rango de funciones que los SNN pueden aproximar es más limitado en comparación con las redes neuronales artificiales (ANN) tradicionales [11]. Antes de que NC pueda tener un impacto más significativo en la IA y la tecnología informática, es necesario abordar estos desafíos relacionados con los SNN.This dissertation addresses current limitations of neuromorphic computing to create energy-efficient and adaptable artificial intelligence systems. It focuses on increasing utilization of neuromorphic computing by designing novel architectures that improve the performance of the spiking neural networks. Specifically, the architectures address the issues of training complexity, hyperparameter selection, computational flexibility, and scarcity of training data. The first proposed architecture utilizes auxiliary learning to improve training performance and data usage, while the second architecture leverages neuromodulation capability of spiking neurons to improve multitasking classification performance. The proposed architectures are tested on the Intel’s Loihi2 neuromorphic computer using several neuromorphic data sets, such as NMIST, DVSCIFAR10, and DVS128-Gesture. Results presented in this dissertation demonstrate the potential of the proposed architectures, but also reveal some limitations that are proposed as future work

Deep Learning and Polar Transformation to Achieve a Novel Adaptive Automatic Modulation Classification Framework

Automatic modulation classification (AMC) is an approach that can be leveraged to identify an observed signal\u27s most likely employed modulation scheme without any a priori knowledge of the intercepted signal. Of the three primary approaches proposed in literature, which are likelihood-based, distribution test-based, and feature-based (FB), the latter is considered to be the most promising approach for real-world implementations due to its favorable computational complexity and classification accuracy. FB AMC is comprised of two stages: feature extraction and labeling. In this thesis, we enhance the FB approach in both stages. In the feature extraction stage, we propose a new architecture in which it first removes the bias issue for the estimator of fourth-order cumulants, then extracts polar-transformed information of the received IQ waveform\u27s samples, and finally forms a unique dataset to be used in the labeling stage. The labeling stage utilizes a deep learning architecture. Furthermore, we propose a new approach to increasing the classification accuracy in low signal-to-noise ratio conditions by employing a deep belief network platform in addition to the spiking neural network platform to overcome computational complexity concerns associated with deep learning architecture. In the process of evaluating the contributions, we first study each individual FB AMC classifier to derive the respective upper and lower performance bounds. We then propose an adaptive framework that is built upon and developed around these findings. This framework aims to efficiently classify the received signal\u27s modulation scheme by intelligently switching between these different FB classifiers to achieve an optimal balance between classification accuracy and computational complexity for any observed channel conditions derived from the main receiver\u27s equalizer. This framework also provides flexibility in deploying FB AMC classifiers in various environments. We conduct a performance analysis using this framework in which we employ the standard RadioML dataset to achieve a realistic evaluation. Numerical results indicate a notably higher classification accuracy by 16.02% on average when the deep belief network is employed, whereas the spiking neural network requires significantly less computational complexity by 34.31% to label the modulation scheme compared to the other platforms. Moreover, the analysis of employing framework exhibits higher efficiency versus employing an individual FB AMC classifier. Advisor: Hamid R. Sharif-Kashan

Enhancing Neuromorphic Computing with Advanced Spiking Neural Network Architectures

This dissertation proposes ways to address current limitations of neuromorphic computing to create energy-efficient and adaptable systems for AI applications. It does so by designing novel spiking neural networks architectures that improve their performance. Specifically, the two proposed architectures address the issues of training complexity, hyperparameter selection, computational flexibility, and scarcity of neuromorphic training data. The first architecture uses auxiliary learning to improve training performance and data usage, while the second architecture leverages neuromodulation capability of spiking neurons to improve multitasking classification performance. The proposed architectures are tested on Intel\u27s Loihi2 neuromorphic chip using several neuromorphic datasets, such as NMIST, DVSCIFAR10, and DVS128-Gesture. The presented results demonstrate potential of the proposed architectures but also reveal some of their limitations which are proposed as future research

Object detection and recognition with event driven cameras

This thesis presents study, analysis and implementation of algorithms to perform object detection and recognition using an event-based cam era. This sensor represents a novel paradigm which opens a wide range of possibilities for future developments of computer vision. In partic ular it allows to produce a fast, compressed, illumination invariant output, which can be exploited for robotic tasks, where fast dynamics and signi\ufb01cant illumination changes are frequent. The experiments are carried out on the neuromorphic version of the iCub humanoid platform. The robot is equipped with a novel dual camera setup mounted directly in the robot\u2019s eyes, used to generate data with a moving camera. The motion causes the presence of background clut ter in the event stream. In such scenario the detection problem has been addressed with an at tention mechanism, speci\ufb01cally designed to respond to the presence of objects, while discarding clutter. The proposed implementation takes advantage of the nature of the data to simplify the original proto object saliency model which inspired this work. Successively, the recognition task was \ufb01rst tackled with a feasibility study to demonstrate that the event stream carries su\ufb03cient informa tion to classify objects and then with the implementation of a spiking neural network. The feasibility study provides the proof-of-concept that events are informative enough in the context of object classi\ufb01 cation, whereas the spiking implementation improves the results by employing an architecture speci\ufb01cally designed to process event data. The spiking network was trained with a three-factor local learning rule which overcomes weight transport, update locking and non-locality problem. The presented results prove that both detection and classi\ufb01cation can be carried-out in the target application using the event data

A Novel Method for Intelligent Single Fault Detection of Bearings Using SAE and Improved D–S Evidence Theory

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster–Shafer evidence theory (D–S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors’ data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D–S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases; when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types’ fault detection accuracy—reached to 99.12%, 99.33% and 98.46% by the improved Dempster–Shafer evidence theory (IDS) to fuse the sensors’ results—is respectively 0.38%, 2.06% and 0.76% higher than the traditional D–S evidence theory. That indicated the effectiveness of improving the D–S evidence theory by evidence weight calculation of PCC