395 research outputs found

    Implementing an Insect Brain Computational Circuit Using III–V Nanowire Components in a Single Shared Waveguide Optical Network

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    Recent developments in photonics include efficient nanoscale optoelectronic components and novel methods for sub-wavelength light manipulation. Here, we explore the potential offered by such devices as a substrate for neuromorphic computing. We propose an artificial neural network in which the weighted connectivity between nodes is achieved by emitting and receiving overlapping light signals inside a shared quasi 2D waveguide. This decreases the circuit footprint by at least an order of magnitude compared to existing optical solutions. The reception, evaluation and emission of the optical signals are performed by a neuron-like node constructed from known, highly efficient III-V nanowire optoelectronics. This minimizes power consumption of the network. To demonstrate the concept, we build a computational model based on an anatomically correct, functioning model of the central-complex navigation circuit of the insect brain. We simulate in detail the optical and electronic parts required to reproduce the connectivity of the central part of this network, using experimentally derived parameters. The results are used as input in the full model and we demonstrate that the functionality is preserved. Our approach points to a general method for drastically reducing the footprint and improving power efficiency of optoelectronic neural networks, leveraging the superior speed and energy efficiency of light as a carrier of information.Comment: 28 pages, 6 figures; supplementary information 15 pages, 8 figure

    LOW POWER AND HIGH SIGNAL TO NOISE RATIO BIO-MEDICAL AFE DESIGN TECHNIQUES

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    The research work described in this thesis was focused on finding novel techniques to implement a low-power and noise Bio-Medical Analog Front End (BMEF) circuit technique to enable high-quality Electrocardiography (ECG) sensing. Usually, an ECG signal and several bio-medical signals are sensed from the human body through a pair of electrodes. The electrical characteristics of the very small amplitude (1u-10mV) signals are corrupted by random noise and have a significant dc offset. 50/60Hz power supply coupling noise is one of the biggest cross-talk signals compared to the thermally generated random noise. These signals are even AFE composed of an Instrumentation Amplifier (IA), which will have a better Common Mode rejection ratio (CMRR). The main function of the AFE is to convert the weak electrical Signal into large signals whose amplitude is large enough for an Analog Digital Converter (ADC) to detect without having any errors. A Variable Gain Amplifier (VGA) is sometimes required to adjust signal amplitude to maintain the dynamic range of the ADC. Also, the Bio-medical transceiver needs an accurate and temperature-independent reference voltage and current for the ADC, commonly known as Bandgap Reference Circuit (BGR). These circuits need to consume as low power as possible to enable these circuits to be powered from the battery. The work started with analysing the existing circuit techniques for the circuits mentioned above and finding the key important improvements required to reach the target specifications. Previously proposed IA is generated based on voltage mode signal processing. To improve the CMRR (119dB), we proposed a current mode-based IA with an embedded DC cancellation technique. State-of-the-art VGA circuits were built based on the degeneration principle of the differential pair, which will enable the variable gain purpose, but none of these techniques discussed linearity improvement, which is very important in modern CMOS technologies. This work enhances the total Harmonic distortion (THD) by 21dB in the worst case by exploiting the feedback techniques around the differential pair. Also, this work proposes a low power curvature compensated bandgap with 2ppm/0C temperature sensitivity while consuming 12.5uW power from a 1.2V dc power supply. All circuits were built in 45nm TSMC-CMOS technology and simulated with all the performance metrics with Cadence (spectre) simulator. The circuit layout was carried out to study post-layout parasitic effect sensitivity

    Roadmap on semiconductor-cell biointerfaces.

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    This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

    Memristors

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    This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments

    Integrated Circuits and Systems for Smart Sensory Applications

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    Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

    Low power CMOS IC, biosensor and wireless power transfer techniques for wireless sensor network application

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    The emerging field of wireless sensor network (WSN) is receiving great attention due to the interest in healthcare. Traditional battery-powered devices suffer from large size, weight and secondary replacement surgery after the battery life-time which is often not desired, especially for an implantable application. Thus an energy harvesting method needs to be investigated. In addition to energy harvesting, the sensor network needs to be low power to extend the wireless power transfer distance and meet the regulation on RF power exposed to human tissue (specific absorption ratio). Also, miniature sensor integration is another challenge since most of the commercial sensors have rigid form or have a bulky size. The objective of this thesis is to provide solutions to the aforementioned challenges

    Towards Oxide Electronics:a Roadmap

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    At the end of a rush lasting over half a century, in which CMOS technology has been experiencing a constant and breathtaking increase of device speed and density, Moore's law is approaching the insurmountable barrier given by the ultimate atomic nature of matter. A major challenge for 21st century scientists is finding novel strategies, concepts and materials for replacing silicon-based CMOS semiconductor technologies and guaranteeing a continued and steady technological progress in next decades. Among the materials classes candidate to contribute to this momentous challenge, oxide films and heterostructures are a particularly appealing hunting ground. The vastity, intended in pure chemical terms, of this class of compounds, the complexity of their correlated behaviour, and the wealth of functional properties they display, has already made these systems the subject of choice, worldwide, of a strongly networked, dynamic and interdisciplinary research community. Oxide science and technology has been the target of a wide four-year project, named Towards Oxide-Based Electronics (TO-BE), that has been recently running in Europe and has involved as participants several hundred scientists from 29 EU countries. In this review and perspective paper, published as a final deliverable of the TO-BE Action, the opportunities of oxides as future electronic materials for Information and Communication Technologies ICT and Energy are discussed. The paper is organized as a set of contributions, all selected and ordered as individual building blocks of a wider general scheme. After a brief preface by the editors and an introductory contribution, two sections follow. The first is mainly devoted to providing a perspective on the latest theoretical and experimental methods that are employed to investigate oxides and to produce oxide-based films, heterostructures and devices. In the second, all contributions are dedicated to different specific fields of applications of oxide thin films and heterostructures, in sectors as data storage and computing, optics and plasmonics, magnonics, energy conversion and harvesting, and power electronics

    Subthreshold design of ultra low-power analog modules

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    Il consumo di potenza rappresenta l’indicatore chiave delle performance di recenti applicazioni portatili, come dispositivi medici impiantabili o tag RFID passivi, allo scopo di aumentare, rispettivamente, i tempi di funzionamento o i range operativi. La riduzione della tensione di alimentazione si è dimostrata l’approccio migliore per ridurre il consumo di potenza dei sistemi digitali integrati. Al fine di tenere il passo con la riduzione delle tensioni di alimentazione, anche le sezioni analogiche dei sistemi mixed signal devono essere in grado di funzionare con livelli di tensione molto bassi. Di conseguenza, sono richieste nuove metodologie di progettazione analogica e configurazioni circuitali innovative in grado di lavorare con tensioni di alimentazioni bassissime, dissipando una potenza estremamente bassa. Il regime di funzionamento sottosoglia consente di ridurre notevolmente le tensioni applicabili ai dispositivi ed si contraddistingue per i livelli di corrente molto bassi, rispetto al ben noto funzionamento in forte inversione. Queste due caratteristiche sono state sfruttate nella realizzazione di moduli analogici di base ultra low voltage, low power. Tre nuove architetture di riferimenti di tensione, che lavorano con tutti i transistor polarizzati in regime sottosoglia, sono stati fabbricati in tecnologia CMOS 0.18 μm. I tre circuiti si basano sullo stesso principio di funzionamento per compensare gli effetti della variazione della temperatura sulla tensione di riferimento generata. Tramite il principio di funzionamento proposto, la tensione di riferimento può essere approssimata con la differenza delle tensioni di soglia, a temperatura ambiente, dei transistor. Misure sperimentali sono state effettuate su set con più di 30 campioni per ogni configurazione circuitale. Una dettagliata analisi statistica ha dimostrato un consumo medio di potenza che va da pochi nano watt a poche decine di nano watt, mentre la minima tensione di alimentazione, raggiunta da una delle tre configurazioni, è di soli 0.45 V. Le tensioni di riferimento generate sono molto precise rispetto alle variazioni della temperatura e della tensione di alimentazione, infatti sono stati ottenuti coefficienti di temperatura e line sensitivity medi a partire rispettivamente da 165 ppm/°C e 0.065 %/V. Inoltre, è stata trattata anche la progettazione di amplificatori ultra low voltage, low power. Sono state illustrate linee guida dettagliate per la progettazione di amplificatori sottosoglia e le stesse sono state applicate per la realizzazione di un amplificatore a due stadi, con compensazione di Miller, funzionante con una tensione di alimentazione di 0.5 V. I risultati sperimentali dell’op amp proposto, fabbricato in tecnologia CMOS 0.18 μm, hanno mostrato un guadagno DC ad anello aperto di 70 dB, un prodotto banda guadagno di 18 kHz ed un consumo di potenza di soli 75 nW. I risultati delle misure sperimentali dimostrano che gli amplificatori operazionali in sottosoglia rappresentano una soluzione molto interessante nella realizzazione di applicazioni efficienti in termini energetici per gli attuali sistemi elettronici portatili. Dal confronto con amplificatori ultra low power, low voltage presenti in letteratura, si evince che la soluzione proposta offre un miglior compromesso tra velocità, potenza dissipata e capacità di carico

    Efficient machine learning: models and accelerations

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    One of the key enablers of the recent unprecedented success of machine learning is the adoption of very large models. Modern machine learning models typically consist of multiple cascaded layers such as deep neural networks, and at least millions to hundreds of millions of parameters (i.e., weights) for the entire model. The larger-scale model tend to enable the extraction of more complex high-level features, and therefore, lead to a significant improvement of the overall accuracy. On the other side, the layered deep structure and large model sizes also demand to increase computational capability and memory requirements. In order to achieve higher scalability, performance, and energy efficiency for deep learning systems, two orthogonal research and development trends have attracted enormous interests. The first trend is the acceleration while the second is the model compression. The underlying goal of these two trends is the high quality of the models to provides accurate predictions. In this thesis, we address these two problems and utilize different computing paradigms to solve real-life deep learning problems. To explore in these two domains, this thesis first presents the cogent confabulation network for sentence completion problem. We use Chinese language as a case study to describe our exploration of the cogent confabulation based text recognition models. The exploration and optimization of the cogent confabulation based models have been conducted through various comparisons. The optimized network offered a better accuracy performance for the sentence completion. To accelerate the sentence completion problem in a multi-processing system, we propose a parallel framework for the confabulation recall algorithm. The parallel implementation reduce runtime, improve the recall accuracy by breaking the fixed evaluation order and introducing more generalization, and maintain a balanced progress in status update among all neurons. A lexicon scheduling algorithm is presented to further improve the model performance. As deep neural networks have been proven effective to solve many real-life applications, and they are deployed on low-power devices, we then investigated the acceleration for the neural network inference using a hardware-friendly computing paradigm, stochastic computing. It is an approximate computing paradigm which requires small hardware footprint and achieves high energy efficiency. Applying this stochastic computing to deep convolutional neural networks, we design the functional hardware blocks and optimize them jointly to minimize the accuracy loss due to the approximation. The synthesis results show that the proposed design achieves the remarkable low hardware cost and power/energy consumption. Modern neural networks usually imply a huge amount of parameters which cannot be fit into embedded devices. Compression of the deep learning models together with acceleration attracts our attention. We introduce the structured matrices based neural network to address this problem. Circulant matrix is one of the structured matrices, where a matrix can be represented using a single vector, so that the matrix is compressed. We further investigate a more flexible structure based on circulant matrix, called block-circulant matrix. It partitions a matrix into several smaller blocks and makes each submatrix is circulant. The compression ratio is controllable. With the help of Fourier Transform based equivalent computation, the inference of the deep neural network can be accelerated energy efficiently on the FPGAs. We also offer the optimization for the training algorithm for block circulant matrices based neural networks to obtain a high accuracy after compression
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