Domain-Specific Computing Architectures and Paradigms

We live in an exciting era where artificial intelligence (AI) is fundamentally shifting the dynamics of industries and businesses around the world. AI algorithms such as deep learning (DL) have drastically advanced the state-of-the-art cognition and learning capabilities. However, the power of modern AI algorithms can only be enabled if the underlying domain-specific computing hardware can deliver orders of magnitude more performance and energy efficiency. This work focuses on this goal and explores three parts of the domain-specific computing acceleration problem; encapsulating specialized hardware and software architectures and paradigms that support the ever-growing processing demand of modern AI applications from the edge to the cloud. This first part of this work investigates the optimizations of a sparse spatio-temporal (ST) cognitive system-on-a-chip (SoC). This design extracts ST features from videos and leverages sparse inference and kernel compression to efficiently perform action classification and motion tracking. The second part of this work explores the significance of dataflows and reduction mechanisms for sparse deep neural network (DNN) acceleration. This design features a dynamic, look-ahead index matching unit in hardware to efficiently discover fine-grained parallelism, achieving high energy efficiency and low control complexity for a wide variety of DNN layers. Lastly, this work expands the scope to real-time machine learning (RTML) acceleration. A new high-level architecture modeling framework is proposed. Specifically, this framework consists of a set of high-performance RTML-specific architecture design templates, and a Python-based high-level modeling and compiler tool chain for efficient cross-stack architecture design and exploration.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162870/1/lchingen_1.pd

高速ビジョンを用いたリアルタイムビデオモザイキングと安定化に関する研究

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Object detection in robotics using morphological information

Mestrado em Engenharia Electrónica e TelecomunicaçõesUma das componentes mais importantes em sistemas de processamento de imagem é a detecção de objectos de interesse. Contudo, a detecção de objectos é um desafio. Dada uma imagem arbitrária e assumindo que se está interessado em localizar um determinado objecto, o grande objectivo da detecção de objectos passa por determinar se existe ou não qualquer objecto de interesse. Esta tese encontra-se inserida no domínio do RoboCup e foca o desenvolvimento de algoritmos para a detecção de bolas oficiais da FIFA, um objecto importante no futebol robótico. Para atingir o objectivo principal, foram desenvolvidos três algoritmos para detectar bolas de futebol com cores arbitrárias, usando informação morfológica obtida através do detector de cortornos Canny e da tranformada de Hough. Em primeiro lugar, foi desenvolvida uma abordagem onde se implementou um algoritmo específico usando a transformada de Hough circular. Em segundo lugar, foi implementado um algoritmo que utiliza uma função da biblioteca OpenCV dedicada à procura de círculos em imagens. Finalmente, os dois primeiros algoritmos foram agrupados para criar uma nova abordagem, na qual ambos os algoritmos são usados. São apresentados resultados experimentais que mostram que os algoritmos desenvolvidos são precisos, sendo capazes de realizar a detecção da bola de forma confiável em situações de tempo-real. ABSTRACT: One of the most important steps in image processing systems is the detection of objects of interest. However, object detection is a challenging task. Given an arbitrary image and assuming that we are interested in locating a particular object, the goal of object detection is to determine whether or not there is any object of interest. This thesis is inserted in the RoboCup domain and is focused on the development of algorithms for the detection of arbitrary FIFA balls, an important object for soccer robots. To achieve the main objective, we developed three algorithms to detect arbitrary soccer balls using morphological information given by the Canny edge detector and the Hough Transform. First, it was developed an approach where we implemented a specific algorithm using the circular Hough Transform, applied after the segmentation of the acquired image. Secondly, it was implemented an algorithm that uses a function of the OpenCV library dedicated to the search of circles in images. Finally, the two first algorithms were joined to create a new approach in which both of the algorithms are used. Experimental results are presented, showing that the developed algorithms are accurate, being capable of reliable ball detection in real-time situations

Component-Level Electronic-Assembly Repair (CLEAR) Operational Concept

This Component-Level Electronic-Assembly Repair (CLEAR) Operational Concept document was developed as a first step in developing the Component-Level Electronic-Assembly Repair (CLEAR) System Architecture (NASA/TM-2011-216956). The CLEAR operational concept defines how the system will be used by the Constellation Program and what needs it meets. The document creates scenarios for major elements of the CLEAR architecture. These scenarios are generic enough to apply to near-Earth, Moon, and Mars missions. The CLEAR operational concept involves basic assumptions about the overall program architecture and interactions with the CLEAR system architecture. The assumptions include spacecraft and operational constraints for near-Earth orbit, Moon, and Mars missions. This document addresses an incremental development strategy where capabilities evolve over time, but it is structured to prevent obsolescence. The approach minimizes flight hardware by exploiting Internet-like telecommunications that enables CLEAR capabilities to remain on Earth and to be uplinked as needed. To minimize crew time and operational cost, CLEAR exploits offline development and validation to support online teleoperations. Operational concept scenarios are developed for diagnostics, repair, and functional test operations. Many of the supporting functions defined in these operational scenarios are further defined as technologies in NASA/TM-2011-216956

Odometria visual monocular em robôs para a agricultura com camara(s) com lentes "olho de peixe"

One of the main challenges in robotics is to develop accurate localization methods that achieve acceptable runtime performances.One of the most common approaches is to use Global Navigation Satellite System such as GPS to localize robots.However, satellite signals are not full-time available in some kind of environments.The purpose of this dissertation is to develop a localization system for a ground robot.This robot is inserted in a project called RoMoVi and is intended to perform tasks like crop monitoring and harvesting in steep slope vineyards.This vineyards are localized in the Douro region which are characterized by the presence of high hills.Thus, the context of RoMoVi is not prosperous for the use of GPS-based localization systems.Therefore, the main goal of this work is to create a reliable localization system based on vision techniques and low cost sensors.To do so, a Visual Odometry system will be used.The concept of Visual Odometry is equivalent to wheel odometry but it has the advantage of not suffering from wheel slip which is present in these kind of environments due to the harsh terrain conditions.Here, motion is tracked computing the homogeneous transformation between camera frames, incrementally.However, this approach also presents some open issues.Most of the state of art methods, specially those who present a monocular camera system, don't perform good motion estimations in pure rotations.In some of them, motion even degenerates in these situations.Also, computing the motion scale is a difficult task that is widely investigated in this field.This work is intended to solve these issues.To do so, fisheye lens cameras will be used in order to achieve wide vision field of views

A minimally invasive surgical system for 3D ultrasound guided robotic retrieval of foreign bodies from a beating heart

The result of various medical conditions and trauma, foreign bodies in the heart pose a serious health risk as they may interfere with cardiovascular function. Particles such as thrombi, bullet fragments, and shrapnel can become trapped in a person's heart after migrating through the venous system, or by direct penetration. The severity of disruption can range from benign to fatal, with associated symptoms including anxiety, fever, cardiac tamponade, hemorrhage, infection, embolism, arrhythmia, and valve dysfunction. Injuries of this nature are common in both civilian and military populations. For symptomatic cases, conventional treatment is removal of the foreign body through open surgery via a median sternotomy, the use of cardiopulmonary bypass, and a wide incision in the heart muscle; these methods incur pronounced perioperative risks and long recovery periods. In order to improve upon the standard of care, we propose an image guided robotic system and a corresponding minimally invasive surgical approach. The system employs a dexterous robotic capture device that can maneuver inside the heart through a small incision. Visualization and guidance within the otherwise occluded internal regions are provided by 3D transesophageal echocardiography (TEE), an emerging form of intraoperative medical imaging used in interventions such as mitral valve repair and device implantation. A robotic approach, as opposed to a manual procedure using rigid instruments, is motivated by the various challenges inherent in minimally invasive surgery, which arise from attempts to perform skilled surgical tasks through small incisions without direct vision. Challenges include reduced dexterity, constrained workspace, limited visualization, and difficult hand-eye coordination, which ultimately lead to poor manipulability. A dexterous robotic end effector with real-time image guidance can help overcome these challenges and potentially improve surgical performance. However promising, such a system and approach require that several technical hurdles be resolved. The foreign body must be automatically tracked as it travels about the dynamic environment of the heart. The erratically moving particle must then be captured using a dexterous robot that moves much more slowly in comparison. Furthermore, retrieval must be performed under 3D ultrasound guidance, amidst the uncertainties presented by both the turbulent flow and by the imaging modality itself. In addressing such barriers, this thesis explores the development of a prototype system capable of retrieving a foreign body from a beating heart, culminating in a set of demonstrative in vitro experiments

A model-based design flow for embedded vision applications on heterogeneous architectures

The ability to gather information from images is straightforward to human, and one of the principal input to understand external world. Computer vision (CV) is the process to extract such knowledge from the visual domain in an algorithmic fashion. The requested computational power to process these information is very high. Until recently, the only feasible way to meet non-functional requirements like performance was to develop custom hardware, which is costly, time-consuming and can not be reused in a general purpose. The recent introduction of low-power and low-cost heterogeneous embedded boards, in which CPUs are combine with heterogeneous accelerators like GPUs, DSPs and FPGAs, can combine the hardware efficiency needed for non-functional requirements with the flexibility of software development. Embedded vision is the term used to identify the application of the aforementioned CV algorithms applied in the embedded field, which usually requires to satisfy, other than functional requirements, also non-functional requirements such as real-time performance, power, and energy efficiency. Rapid prototyping, early algorithm parametrization, testing, and validation of complex embedded video applications for such heterogeneous architectures is a very challenging task. This thesis presents a comprehensive framework that: 1) Is based on a model-based paradigm. Differently from the standard approaches at the state of the art that require designers to manually model the algorithm in any programming language, the proposed approach allows for a rapid prototyping, algorithm validation and parametrization in a model-based design environment (i.e., Matlab/Simulink). The framework relies on a multi-level design and verification flow by which the high-level model is then semi-automatically refined towards the final automatic synthesis into the target hardware device. 2) Relies on a polyglot parallel programming model. The proposed model combines different programming languages and environments such as C/C++, OpenMP, PThreads, OpenVX, OpenCV, and CUDA to best exploit different levels of parallelism while guaranteeing a semi-automatic customization. 3) Optimizes the application performance and energy efficiency through a novel algorithm for the mapping and scheduling of the application 3 tasks on the heterogeneous computing elements of the device. Such an algorithm, called exclusive earliest finish time (XEFT), takes into consideration the possible multiple implementation of tasks for different computing elements (e.g., a task primitive for CPU and an equivalent parallel implementation for GPU). It introduces and takes advantage of the notion of exclusive overlap between primitives to improve the load balancing. This thesis is the result of three years of research activity, during which all the incremental steps made to compose the framework have been tested on real case studie