Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing

Reservoir computing (RC) is gaining traction in several signal processing domains, owing to its nonlinear stateful computation, spatiotemporal encoding, and reduced training complexity over recurrent neural networks (RNNs). Previous studies have shown the effectiveness of software-based RCs for a wide spectrum of applications. A parallel body of work indicates that realizing RNN architectures using custom integrated circuits and reconfigurable hardware platforms yields significant improvements in power and latency. In this research, we propose a neuromemristive RC architecture, with doubly twisted toroidal structure, that is validated for biosignal processing applications. We exploit the device mismatch to implement the random weight distributions within the reservoir and propose mixed-signal subthreshold circuits for energy efficiency. A comprehensive analysis is performed to compare the efficiency of the neuromemristive RC architecture in both digital(reconfigurable) and subthreshold mixed-signal realizations. Both EEG and EMG biosignal benchmarks are used for validating the RC designs. The proposed RC architecture demonstrated an accuracy of 90% and 84% for epileptic seizure detection and EMG prosthetic finger control respectively

Μελέτη και βελτιστοποίηση οπτικών και υπολογιστικών σχημάτων Reservoir-Computing για την πρόγνωση χρονοεξαρτώμενων σειρών narma

Τα Reservoir Computing (RC) είναι ένα παράδειγμα εκπαίδευσης νευρωνικών δικτύων με ανάδραση (RNN) που βασίζεται στην επεξεργασία του reservoir. Αυτή η τεχνολογία ξεκίνησε πριν 10 χρόνια και σήμερα είναι ένας παραγωγικός χώρος εργασίας πολλά υποσχόμενος, δίνοντας σημαντικές πληροφορίες σχετικά με τα RNNs, εργαλεία μάθησης, καθώς και δυνατότητα υπολογισμού με μη συμβατικά υλικά. Το αντικείμενο της διπλωματικής εργασίας είναι η μελέτη των Reservoir Computing. Αυτή η εργασία δίνει μια γενική εικόνα της τρέχουσας έρευνας για την θεωρία (κεφάλαιο 1) και παρουσιάζονται διάφορες εφαρμογές στον τομέα της ιατρικής, του ήχου, στο χρηματιστήριο και στην ρομποτική (κεφάλαιο 2). Επίσης αναφέρθηκαν μελέτες πάνω στα φωτονικά Reservoir Computing που προσφέρουν την υπόσχεση για μαζική παράλληλη επεξεργασία πληροφοριών με χαμηλή ισχύ και υψηλή ταχύτητα (κεφάλαιο 4). Συγκεκριμένα, έγινε μελέτη της απόδοσης των Reservoir Computing, με την χρήση του προγράμματος MATLAB (κεφάλαιο 3). Φορτώθηκε ένα σύνολο δεδομένων, δημιουργήθηκε ένα δίκτυο, εκπαιδεύει ένα ESN και σχεδιάζει την έξοδο του δικτύου. Μεταβλήθηκαν διάφοροι παράμετροι του δικτύου, φασματική ακτίνα, εσωτερικοί κόμβοι, συνδεσιμότητα, system order και συνάρτηση ενεργοποίησης reservoir, και μέσω των αποτελεσμάτων του τρεξίματος του κώδικα, train NMRSE και test NMRSE, αξιολογήθηκε η απόδοση. Η μεθοδολογία αυτή μπορεί να γίνει οδηγός για την πραγματοποίηση και άλλων μετρήσεων, με απλές αλλαγές στον κώδικα.Reservoir Computing (RC) is a paradigm of training Recurrent Neural Networks (RNNs) based on processing the reservoir. That’s technology started ten years ago and is currently a prolific research area promising, giving important insights into RNNs, learning tools, as well as enabling computation with non-conventional hardware. The object of this thesis is to learning the Reservoir Computing. This object will give an overview of current research on theory (chapter 1) and implementations of medicine, sound stock market and robotics (chapter 2). Also reported studies on the photonic Reservoir Computing offering the promise of massive parallel processing of information with low power and high speed (chapter 4). Particularly, learning the performance of Reservoir Computing, using the MATLAB program (chapter 3). Load a dataset, create a network, a train an ESN and plot the output of the network. Changed various network parameters, spectral radius, internal nodes, connectivity, system order and activation function reservoir, and through the results of running the code, train NMRSE and test NMRSE, the performance was evaluated. This methodology can be used a guide for making other measurements, with making simple changes at the code

A Contribution Towards Intelligent Autonomous Sensors Based on Perovskite Solar Cells and Ta2O5/ZnO Thin Film Transistors

Many broad applications in the field of robotics, brain-machine interfaces, cognitive computing, image and speech processing and wearables require edge devices with very constrained power and hardware requirements that are challenging to realize. This is because these applications require sub-conscious awareness and require to be always “on”, especially when integrated with a sensor node that detects an event in the environment. Present day edge intelligent devices are typically based on hybrid CMOS-memristor arrays that have been so far designed for fast switching, typically in the range of nanoseconds, low energy consumption (typically in nano-Joules), high density and endurance (exceeding 1015 cycles). On the other hand, sensory-processing systems that have the same time constants and dynamics as their input signals, are best placed to learn or extract information from them. To meet this requirement, many applications are implemented using external “delay” in the memristor, in a process which enables each synapse to be modeled as a combination of a temporal delay and a spatial weight parameter. This thesis demonstrates a synaptic thin film transistor capable of inherent logic functions as well as compute-in-memory on similar time scales as biological events. Even beyond a conventional crossbar array architecture, we have relied on new concepts in reservoir computing to demonstrate a delay system reservoir with the highest learning efficiency of 95% reported to date, in comparison to equivalent two terminal memristors, using a single device for the task of image processing. The crux of our findings relied on enhancing our capability to model the unique physics of the device, in the scope of the current thesis, that is not amenable to conventional TCAD simulations. The model provides new insight into the redox characteristics of the gate current and paves way for assessment of device performance in compute-in-memory applications. The diffusion-based mechanism of the device, effectively enables time constants that have potential in applications such as gesture recognition and detection of cardiac arrythmia. The thesis also reports a new orientation of a solution processed perovskite solar cell with an efficiency of 14.9% that is easily integrable into an intelligent sensor node. We examine the influence of the growth orientation on film morphology and solar cell efficiency. Collectively, our work aids the development of more energy-efficient, powerful edge-computing sensor systems for upcoming applications of the IOT

Energetically deposited tin oxide: characterization and device applications

Semiconductor oxides are promising materials that have made impressive progress in recent years, challenging the dominance of silicon not only in conventional devices including field-effect transistors but being amenable to next-generation electronic devices such as memristors. Although a variety of oxides have been explored, tin oxide has been an interesting material for researchers when offering p-type characteristics of tin monoxide SnO and n-type characteristics in tin dioxide SnO2. While SnO2 is easy to grow and well suited for a wide range of applications, it is difficult to form p-type SnO due to its metastability where it forms into the more stable phase SnO2. The work presented in this Doctoral Dissertation focus on exploring the characteristics and applications of energetically deposited tin oxide thin films. The tin oxide film deposited using high-power impulse magnetron sputtering was found to be mixed-phase nanocrystalline SnO and SnO2 in which SnO2 is dominant. The high resistivity, low carrier concentration and low mobility in the as-deposited and annealed samples hindered the application of the high-power impulse magnetron sputtering (HiPIMS) SnOx in thin film transistors, however, suggested suitability for these films as a memristive material. A small but quantifiable variation in film stoichiometry (Sn:O) resulting from the off-axis deposition led to the formation of two different types of memristive devices, namely filamentary and nanoparticle network memristors. Both devices exhibited stable volatile bidirectional resistive switching with a ratio between high resistance and low resistance of more than two orders of magnitude. However, their underlying resistive switching mechanisms and device characteristics were significantly different. Synaptic-like behaviours were observed on both filamentary devices (FDs) and nanoparticle network devices (NNDs), highlighting their potential for information processing in neuromorphic computing systems. While a FD can become only an individual cell in reservoir computing circuits, an NND can be implemented as a reservoir due to their available inter-connectivity which is required for reservoir computing