170 research outputs found
RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults
To maximize the performance and energy efficiency of Spiking Neural Network
(SNN) processing on resource-constrained embedded systems, specialized hardware
accelerators/chips are employed. However, these SNN chips may suffer from
permanent faults which can affect the functionality of weight memory and neuron
behavior, thereby causing potentially significant accuracy degradation and
system malfunctioning. Such permanent faults may come from manufacturing
defects during the fabrication process, and/or from device/transistor damages
(e.g., due to wear out) during the run-time operation. However, the impact of
permanent faults in SNN chips and the respective mitigation techniques have not
been thoroughly investigated yet. Toward this, we propose RescueSNN, a novel
methodology to mitigate permanent faults in the compute engine of SNN chips
without requiring additional retraining, thereby significantly cutting down the
design time and retraining costs, while maintaining the throughput and quality.
The key ideas of our RescueSNN methodology are (1) analyzing the
characteristics of SNN under permanent faults; (2) leveraging this analysis to
improve the SNN fault-tolerance through effective fault-aware mapping (FAM);
and (3) devising lightweight hardware enhancements to support FAM. Our FAM
technique leverages the fault map of SNN compute engine for (i) minimizing
weight corruption when mapping weight bits on the faulty memory cells, and (ii)
selectively employing faulty neurons that do not cause significant accuracy
degradation to maintain accuracy and throughput, while considering the SNN
operations and processing dataflow. The experimental results show that our
RescueSNN improves accuracy by up to 80% while maintaining the throughput
reduction below 25% in high fault rate (e.g., 0.5 of the potential fault
locations), as compared to running SNNs on the faulty chip without mitigation.Comment: Accepted for publication at Frontiers in Neuroscience - Section
Neuromorphic Engineerin
MOCAST 2021
The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications
Biomanufacturing Technologies for Tissue Engineering
Il seguente lavoro di tesi ha come obiettivo lo studio e la realizzazione di device biomedicali realizzati tramite la manifattura additiva. La manifattura additiva sta avendo una forte crescita negli ultimi anni grazie soprattutto alla possibilità di realizzare facilmente geometrie complesse. Questa caratteristica permette di personalizzare i prodotti ad un costo competitivo. Inoltre, lo spreco di materiale viene ridotto moltissimo dal principio di fabbricazione. Tutte queste proprietà hanno fatto in modo che negli ultimi anni la manifattura additiva prendesse sempre più piede in campi come l’automotive, l’aerospace e il biomedicale.
Questo lavoro di tesi è focalizzato sull’utilizzo di alcune tra le più diffuse tecnologie additive per la produzione di device biomedicali. In particolare, il lavoro si è concentrato principalmente sulla realizzazione di due modelli, il primo per lo studio dello sviluppo dei black floaters all’interno del corpo vitreo dell’occhio, il secondo per l’emulazione del comportamento dell’osso mandibolare durante la foratura per l’installazione di impianti dentali.
Il modello dell’occhio è composto da due elementi principali, un supporto e un hydrogel. Il supporto serve a contenere e supportare l’hydrogel. Deve essere trasparente, biocompatibile facilmente manovrabile in laboratorio. La sua realizzazione è avvenuta tramite stereolitografia. L’hydrogel, invece, ha lo scopo di fornire un’ambiente 3D per la crescita e sviluppo delle cellule. Deve perciò anche lui essere biocompatibile e con adeguate caratteristiche meccaniche e di stampabilità . La struttura 3D è stata realizzata tramite material extrusion.
Il modello di osso mandibolare è stato realizzato tramite fused filament fabrication. Il modello si compone di due parti, una parte esterna piena per emulare l’osso corticale, e una parte interna porosa per emulare l’osso trabecolare. Le prove di foratura sono state realizzate con un trapano dentistico agganciato a robot collaborativi.
La ricerca ha infine toccato ulteriori due ambiti, lo studio delle proprietà di strutture lattice realizzate tramite laser based- powder bed fusion e la valutazione di diversi trattamenti di finitura superficiale.
La tesi, dunque, ha la seguente organizzazione. Il capitolo 1 presenta un’introduzione sull’additive manufacturing e il bioprinting. Le tecnologie ed i materiali utilizzati sono brevemente descritti e sono riportati alcuni esempi di applicazione della manifattura additiva nel campo biomedicale. I capitoli seguenti, invece, riportano gli articoli pubblicati o in corso di pubblicazione riguardo alle diverse tematiche affrontate. Nello specifico, il capitolo 2 riporta la ricerca sulle strutture lattice e la loro realizzazione. I capitoli 3 e 4 comprendono gli studi relativi al modello dell’occhio. Il capitolo 3 si concentra sulla realizzazione del supporto, il 4 sulla formulazione e la valutazione dell’hydrogel. Il capitolo 5 approfondisce lo studio del modello per l’emulazione del comportamento dell’osso mandibolare a foratura mentre il capitolo 6, l’ultimo di questo elaborato, si concentra sui processi di finitura superficiale.
Per concludere, la manifattura additiva include processi molto diversi tra loro, ma che presentano molti punti in comune come la flessibilità , libertà di progettazione e personalizzazione. Sfruttando queste proprietà è possibile realizzare oggetti su misura, soprattutto in campi come quello biomedicale dove la personalizzazione e la specificità sono fondamentali.The following thesis aims to study and to develop biomedical devices made through additive manufacturing. Additive manufacturing has been experiencing a strong growth in recent years, mainly due to its ability to easily realize complex geometries. This feature allows customization of products at a competitive cost. In addition, material waste is greatly reduced by the manufacturing principle. All these properties helped the recent years diffusion of additive manufacturing in fields such as automotive, aerospace and biomedical.
This thesis focuses on the use of some of the most popular additive technologies for the production of biomedical devices. In particular, the work focused mainly on the fabrication of two models, the first to study the development of black floaters within the vitreous body of the eye, and the second to emulate the mandibular bone behavior during drilling for the installation of dental implants.
The eye model consists of two main elements, a scaffold and a hydrogel. The scaffold contains and provides support to the hydrogel. It must be transparent, biocompatible easily handled in the laboratory. It is printed by stereolithography. The hydrogel, on the other hand, is intended to provide a 3D environment for cell growth and development. Therefore, it must be biocompatible and have adequate mechanical properties together with good printability. The 3D scaffold structure was made by material extrusion.
The mandibular bone model was made by fused filament fabrication. The model consists of two parts, a solid outer part to emulate cortical bone, and a porous inner part to emulate trabecular bone. Drilling tests were performed with a dental drill attached to collaborative robots.
Finally, the research covered two additional areas, the study of the properties of lattice structures made by laser-based- powder bed fusion and the evaluation of different surface finish treatments.
The following thesis, therefore, has the following organization. Chapter 1 presents an introduction on additive manufacturing and bioprinting. The technologies and materials used are briefly described, and examples of additive manufacturing applications in the biomedical field are given. The following chapters, on the other hand, report published or forthcoming articles regarding the various topics mentioned above. Specifically, Chapter 2 reports the research on lattice structures and their fabrication. Chapters 3 and 4 include studies related to the eye model. Chapter 3 focuses on the fabrication of the support, and Chapter 4 on the formulation and evaluation of the hydrogel. Chapter 5 presents the study of the model for emulating the behavior of mandibular bone upon drilling, while Chapter 6, the last of this work, focuses on surface finishing processes.
In conclusion, additive manufacturing includes various processes that are very different from each other but have many common points such as flexibility, freedom of design, and customization. By exploiting these properties, it is possible to make tailored objects, especially important in fields such as the biomedical one, where customization and specificity are a great added value
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Reservoir computing (RC), first applied to temporal signal processing, is a
recurrent neural network in which neurons are randomly connected. Once
initialized, the connection strengths remain unchanged. Such a simple structure
turns RC into a non-linear dynamical system that maps low-dimensional inputs
into a high-dimensional space. The model's rich dynamics, linear separability,
and memory capacity then enable a simple linear readout to generate adequate
responses for various applications. RC spans areas far beyond machine learning,
since it has been shown that the complex dynamics can be realized in various
physical hardware implementations and biological devices. This yields greater
flexibility and shorter computation time. Moreover, the neuronal responses
triggered by the model's dynamics shed light on understanding brain mechanisms
that also exploit similar dynamical processes. While the literature on RC is
vast and fragmented, here we conduct a unified review of RC's recent
developments from machine learning to physics, biology, and neuroscience. We
first review the early RC models, and then survey the state-of-the-art models
and their applications. We further introduce studies on modeling the brain's
mechanisms by RC. Finally, we offer new perspectives on RC development,
including reservoir design, coding frameworks unification, physical RC
implementations, and interaction between RC, cognitive neuroscience and
evolution.Comment: 51 pages, 19 figures, IEEE Acces
AI/ML Algorithms and Applications in VLSI Design and Technology
An evident challenge ahead for the integrated circuit (IC) industry in the
nanometer regime is the investigation and development of methods that can
reduce the design complexity ensuing from growing process variations and
curtail the turnaround time of chip manufacturing. Conventional methodologies
employed for such tasks are largely manual; thus, time-consuming and
resource-intensive. In contrast, the unique learning strategies of artificial
intelligence (AI) provide numerous exciting automated approaches for handling
complex and data-intensive tasks in very-large-scale integration (VLSI) design
and testing. Employing AI and machine learning (ML) algorithms in VLSI design
and manufacturing reduces the time and effort for understanding and processing
the data within and across different abstraction levels via automated learning
algorithms. It, in turn, improves the IC yield and reduces the manufacturing
turnaround time. This paper thoroughly reviews the AI/ML automated approaches
introduced in the past towards VLSI design and manufacturing. Moreover, we
discuss the scope of AI/ML applications in the future at various abstraction
levels to revolutionize the field of VLSI design, aiming for high-speed, highly
intelligent, and efficient implementations
Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico
Conference proceedings info:
ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies
Raleigh, HI, United States, March 24-26, 2023
Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center
of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologÃas de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clÃnicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la SecretarÃa de Salud, el Centro de Comando, Comunicaciones y Control Informático.
de la SecretarÃa del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
Understanding Quantum Technologies 2022
Understanding Quantum Technologies 2022 is a creative-commons ebook that
provides a unique 360 degrees overview of quantum technologies from science and
technology to geopolitical and societal issues. It covers quantum physics
history, quantum physics 101, gate-based quantum computing, quantum computing
engineering (including quantum error corrections and quantum computing
energetics), quantum computing hardware (all qubit types, including quantum
annealing and quantum simulation paradigms, history, science, research,
implementation and vendors), quantum enabling technologies (cryogenics, control
electronics, photonics, components fabs, raw materials), quantum computing
algorithms, software development tools and use cases, unconventional computing
(potential alternatives to quantum and classical computing), quantum
telecommunications and cryptography, quantum sensing, quantum technologies
around the world, quantum technologies societal impact and even quantum fake
sciences. The main audience are computer science engineers, developers and IT
specialists as well as quantum scientists and students who want to acquire a
global view of how quantum technologies work, and particularly quantum
computing. This version is an extensive update to the 2021 edition published in
October 2021.Comment: 1132 pages, 920 figures, Letter forma
Silica and Silicon Based Nanostructures
Silica and silicon-based nanostructures are now well-understood materials for which the technologies are mature. The most obvious applications, such as electronic devices, have been widely explored over the last two decades. The aim of this Special Issue is to bring together the state of the art in the field and to enable the emergence of new ideas and concepts for silicon and silica-based nanostructures
2022 roadmap on neuromorphic computing and engineering
Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community
Integrated Circuits and Systems for Smart Sensory Applications
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
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