498 research outputs found

    Tools for efficient Deep Learning

    Get PDF
    In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption. We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work. This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C. Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets. All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces

    Towards trustworthy computing on untrustworthy hardware

    Get PDF
    Historically, hardware was thought to be inherently secure and trusted due to its obscurity and the isolated nature of its design and manufacturing. In the last two decades, however, hardware trust and security have emerged as pressing issues. Modern day hardware is surrounded by threats manifested mainly in undesired modifications by untrusted parties in its supply chain, unauthorized and pirated selling, injected faults, and system and microarchitectural level attacks. These threats, if realized, are expected to push hardware to abnormal and unexpected behaviour causing real-life damage and significantly undermining our trust in the electronic and computing systems we use in our daily lives and in safety critical applications. A large number of detective and preventive countermeasures have been proposed in literature. It is a fact, however, that our knowledge of potential consequences to real-life threats to hardware trust is lacking given the limited number of real-life reports and the plethora of ways in which hardware trust could be undermined. With this in mind, run-time monitoring of hardware combined with active mitigation of attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed as the last line of defence. This last line of defence allows us to face the issue of live hardware mistrust rather than turning a blind eye to it or being helpless once it occurs. This thesis proposes three different frameworks towards trustworthy computing on untrustworthy hardware. The presented frameworks are adaptable to different applications, independent of the design of the monitored elements, based on autonomous security elements, and are computationally lightweight. The first framework is concerned with explicit violations and breaches of trust at run-time, with an untrustworthy on-chip communication interconnect presented as a potential offender. The framework is based on the guiding principles of component guarding, data tagging, and event verification. The second framework targets hardware elements with inherently variable and unpredictable operational latency and proposes a machine-learning based characterization of these latencies to infer undesired latency extensions or denial of service attacks. The framework is implemented on a DDR3 DRAM after showing its vulnerability to obscured latency extension attacks. The third framework studies the possibility of the deployment of untrustworthy hardware elements in the analog front end, and the consequent integrity issues that might arise at the analog-digital boundary of system on chips. The framework uses machine learning methods and the unique temporal and arithmetic features of signals at this boundary to monitor their integrity and assess their trust level

    SoC-based FPGA architecture for image analysis and other highly demanding applications

    Get PDF
    Al giorno d'oggi, lo sviluppo di algoritmi si concentra su calcoli efficienti in termini di prestazioni ed efficienza energetica. Tecnologie come il field programmable gate array (FPGA) e il system on chip (SoC) basato su FPGA (FPGA/SoC) hanno dimostrato la loro capacità di accelerare applicazioni di calcolo intensive risparmiando al contempo il consumo energetico, grazie alla loro capacità di elevato parallelismo e riconfigurazione dell'architettura. Attualmente, i cicli di progettazione esistenti per FPGA/SoC sono lunghi, a causa della complessità dell'architettura. Pertanto, per colmare il divario tra le applicazioni e le architetture FPGA/SoC e ottenere un design hardware efficiente per l'analisi delle immagini e altri applicazioni altamente demandanti utilizzando lo strumento di sintesi di alto livello, vengono prese in considerazione due strategie complementari: tecniche ad hoc e stima delle prestazioni. Per quanto riguarda le tecniche ad-hoc, tre applicazioni molto impegnative sono state accelerate attraverso gli strumenti HLS: discriminatore di forme di impulso per i raggi cosmici, classificazione automatica degli insetti e re-ranking per il recupero delle informazioni, sottolineando i vantaggi quando questo tipo di applicazioni viene attraversato da tecniche di compressione durante il targeting dispositivi FPGA/SoC. Inoltre, in questa tesi viene proposto uno stimatore delle prestazioni per l'accelerazione hardware per prevedere efficacemente l'utilizzo delle risorse e la latenza per FPGA/SoC, costruendo un ponte tra l'applicazione e i domini architetturali. Lo strumento integra modelli analitici per la previsione delle prestazioni e un motore design space explorer (DSE) per fornire approfondimenti di alto livello agli sviluppatori di hardware, composto da due motori indipendenti: DSE basato sull'ottimizzazione a singolo obiettivo e DSE basato sull'ottimizzazione evolutiva multiobiettivo.Nowadays, the development of algorithms focuses on performance-efficient and energy-efficient computations. Technologies such as field programmable gate array (FPGA) and system on chip (SoC) based on FPGA (FPGA/SoC) have shown their ability to accelerate intensive computing applications while saving power consumption, owing to their capability of high parallelism and reconfiguration of the architecture. Currently, the existing design cycles for FPGA/SoC are time-consuming, owing to the complexity of the architecture. Therefore, to address the gap between applications and FPGA/SoC architectures and to obtain an efficient hardware design for image analysis and highly demanding applications using the high-level synthesis tool, two complementary strategies are considered: ad-hoc techniques and performance estimator. Regarding ad-hoc techniques, three highly demanding applications were accelerated through HLS tools: pulse shape discriminator for cosmic rays, automatic pest classification, and re-ranking for information retrieval, emphasizing the benefits when this type of applications are traversed by compression techniques when targeting FPGA/SoC devices. Furthermore, a comprehensive performance estimator for hardware acceleration is proposed in this thesis to effectively predict the resource utilization and latency for FPGA/SoC, building a bridge between the application and architectural domains. The tool integrates analytical models for performance prediction, and a design space explorer (DSE) engine for providing high-level insights to hardware developers, composed of two independent sub-engines: DSE based on single-objective optimization and DSE based on evolutionary multi-objective optimization

    Database System Acceleration on FPGAs

    Get PDF
    Relational database systems provide various services and applications with an efficient means for storing, processing, and retrieving their data. The performance of these systems has a direct impact on the quality of service of the applications that rely on them. Therefore, it is crucial that database systems are able to adapt and grow in tandem with the demands of these applications, ensuring that their performance scales accordingly. In the past, Moore's law and algorithmic advancements have been sufficient to meet these demands. However, with the slowdown of Moore's law, researchers have begun exploring alternative methods, such as application-specific technologies, to satisfy the more challenging performance requirements. One such technology is field-programmable gate arrays (FPGAs), which provide ideal platforms for developing and running custom architectures for accelerating database systems. The goal of this thesis is to develop a domain-specific architecture that can enhance the performance of in-memory database systems when executing analytical queries. Our research is guided by a combination of academic and industrial requirements that seek to strike a balance between generality and performance. The former ensures that our platform can be used to process a diverse range of workloads, while the latter makes it an attractive solution for high-performance use cases. Throughout this thesis, we present the development of a system-on-chip for database system acceleration that meets our requirements. The resulting architecture, called CbMSMK, is capable of processing the projection, sort, aggregation, and equi-join database operators and can also run some complex TPC-H queries. CbMSMK employs a shared sort-merge pipeline for executing all these operators, which results in an efficient use of FPGA resources. This approach enables the instantiation of multiple acceleration cores on the FPGA, allowing it to serve multiple clients simultaneously. CbMSMK can process both arbitrarily deep and wide tables efficiently. The former is achieved through the use of the sort-merge algorithm which utilizes the FPGA RAM for buffering intermediate sort results. The latter is achieved through the use of KeRRaS, a novel variant of the forward radix sort algorithm introduced in this thesis. KeRRaS allows CbMSMK to process a table a few columns at a time, incrementally generating the final result through multiple iterations. Given that acceleration is a key objective of our work, CbMSMK benefits from many performance optimizations. For instance, multi-way merging is employed to reduce the number of merge passes required for the execution of the sort-merge algorithm, thus improving the performance of all our pipeline-breaking operators. Another example is our in-depth analysis of early aggregation, which led to the development of a novel cache-based algorithm that significantly enhances aggregation performance. Our experiments demonstrate that CbMSMK performs on average 5 times faster than the state-of-the-art CPU-based database management system MonetDB.:I Database Systems & FPGAs 1 INTRODUCTION 1.1 Databases & the Importance of Performance 1.2 Accelerators & FPGAs 1.3 Requirements 1.4 Outline & Summary of Contributions 2 BACKGROUND ON DATABASE SYSTEMS 2.1 Databases 2.1.1 Storage Model 2.1.2 Storage Medium 2.2 Database Operators 2.2.1 Projection 2.2.2 Filter 2.2.3 Sort 2.2.4 Aggregation 2.2.5 Join 2.2.6 Operator Classification 2.3 Database Queries 2.4 Impact of Acceleration 3 BACKGROUND ON FPGAS 3.1 FPGA 3.1.1 Logic Element 3.1.2 Block RAM (BRAM) 3.1.3 Digital Signal Processor (DSP) 3.1.4 IO Element 3.1.5 Programmable Interconnect 3.2 FPGADesignFlow 3.2.1 Specifications 3.2.2 RTL Description 3.2.3 Verification 3.2.4 Synthesis, Mapping, Placement, and Routing 3.2.5 TimingAnalysis 3.2.6 Bitstream Generation and FPGA Programming 3.3 Implementation Quality Metrics 3.4 FPGA Cards 3.5 Benefits of Using FPGAs 3.6 Challenges of Using FPGAs 4 RELATED WORK 4.1 Summary of Related Work 4.2 Platform Type 4.2.1 Accelerator Card 4.2.2 Coprocessor 4.2.3 Smart Storage 4.2.4 Network Processor 4.3 Implementation 4.3.1 Loop-based implementation 4.3.2 Sort-based Implementation 4.3.3 Hash-based Implementation 4.3.4 Mixed Implementation 4.4 A Note on Quantitative Performance Comparisons II Cache-Based Morphing Sort-Merge with KeRRaS (CbMSMK) 5 OBJECTIVES AND ARCHITECTURE OVERVIEW 5.1 From Requirements to Objectives 5.2 Architecture Overview 5.3 Outlineof Part II 6 COMPARATIVE ANALYSIS OF OPENCL AND RTL FOR SORT-MERGE PRIMITIVES ON FPGAS 6.1 Programming FPGAs 6.2 RelatedWork 6.3 Architecture 6.3.1 Global Architecture 6.3.2 Sorter Architecture 6.3.3 Merger Architecture 6.3.4 Scalability and Resource Adaptability 6.4 Experiments 6.4.1 OpenCL Sort-Merge Implementation 6.4.2 RTLSorters 6.4.3 RTLMergers 6.4.4 Hybrid OpenCL-RTL Sort-Merge Implementation 6.5 Summary & Discussion 7 RESOURCE-EFFICIENT ACCELERATION OF PIPELINE-BREAKING DATABASE OPERATORS ON FPGAS 7.1 The Case for Resource Efficiency 7.2 Related Work 7.3 Architecture 7.3.1 Sorters 7.3.2 Sort-Network 7.3.3 X:Y Mergers 7.3.4 Merge-Network 7.3.5 Join Materialiser (JoinMat) 7.4 Experiments 7.4.1 Experimental Setup 7.4.2 Implementation Description & Tuning 7.4.3 Sort Benchmarks 7.4.4 Aggregation Benchmarks 7.4.5 Join Benchmarks 7. Summary 8 KERRAS: COLUMN-ORIENTED WIDE TABLE PROCESSING ON FPGAS 8.1 The Scope of Database System Accelerators 8.2 Related Work 8.3 Key-Reduce Radix Sort(KeRRaS) 8.3.1 Time Complexity 8.3.2 Space Complexity (Memory Utilization) 8.3.3 Discussion and Optimizations 8.4 Architecture 8.4.1 MSM 8.4.2 MSMK: Extending MSM with KeRRaS 8.4.3 Payload, Aggregation and Join Processing 8.4.4 Limitations 8.5 Experiments 8.5.1 Experimental Setup 8.5.2 Datasets 8.5.3 MSMK vs. MSM 8.5.4 Payload-Less Benchmarks 8.5.5 Payload-Based Benchmarks 8.5.6 Flexibility 8.6 Summary 9 A STUDY OF EARLY AGGREGATION IN DATABASE QUERY PROCESSING ON FPGAS 9.1 Early Aggregation 9.2 Background & Related Work 9.2.1 Sort-Based Early Aggregation 9.2.2 Cache-Based Early Aggregation 9.3 Simulations 9.3.1 Datasets 9.3.2 Metrics 9.3.3 Sort-Based Versus Cache-Based Early Aggregation 9.3.4 Comparison of Set-Associative Caches 9.3.5 Comparison of Cache Structures 9.3.6 Comparison of Replacement Policies 9.3.7 Cache Selection Methodology 9.4 Cache System Architecture 9.4.1 Window Aggregator 9.4.2 Compressor & Hasher 9.4.3 Collision Detector 9.4.4 Collision Resolver 9.4.5 Cache 9.5 Experiments 9.5.1 Experimental Setup 9.5.2 Resource Utilization and Parameter Tuning 9.5.3 Datasets 9.5.4 Benchmarks on Synthetic Data 9.5.5 Benchmarks on Real Data 9.6 Summary 10 THE FULL PICTURE 10.1 System Architecture 10.2 Benchmarks 10.3 Meeting the Objectives III Conclusion 11 SUMMARY AND OUTLOOK ON FUTURE RESEARCH 11.1 Summary 11.2 Future Work BIBLIOGRAPHY LIST OF FIGURES LIST OF TABLE

    CNNParted: An open source framework for efficient Convolutional Neural Network inference partitioning in embedded systems

    Get PDF
    Applications such as autonomous driving or assistive robotics heavily rely on the usage of Deep Neural Networks. In particular, Convolutional Neural Networks (CNNs) provide precise and reliable results in image processing tasks like camera-based object detection or semantic segmentation. However, to achieve even better results, CNNs are becoming more and more complex. Deploying these networks in distributed embedded systems thereby imposes new challenges, due to additional constraints regarding performance and energy consumption in the near-sensor compute platforms, i.e. the sensor nodes. Processing all data in the central node, however, is disadvantageous since raw data of camera consumes large bandwidth and running CNN inference of multiple tasks requires certain performance. Moreover, sending raw data over the interconnect is not advisable for privacy reasons. Hence, offloading CNN workload to the sensor nodes in the system can lead to reduced traffic on the link and a higher level of data security. However, due to the limited hardware-resources on the sensor nodes, partitioning CNNs has to be done carefully to meet overall latency requirements and energy constraints. Therefore, we present CNNParted, an open-source framework for efficient, hardware-aware CNN inference partitioning targeting embedded AI applications. It automatically searches for potential partitioning points in the CNN to find a beneficial workload distribution between sensor nodes and a central edge node. Thereby, CNNParted not only analyzes the CNN architecture but also takes hardware components, such as dedicated hardware accelerators and memories, into consideration to evaluate inference partitioning regarding latency and energy consumption. Exemplary, we apply CNNParted to three commonly used feed forward CNNs in embedded systems. Thereby, the framework first searches for several potential partitioning points and then evaluates the latter regarding inference latency and energy consumption. Based on the results, beneficial partitioning points can be identified depending on the system constraints. Using the framework, we are able to find and evaluate 10 potential partitioning points for FCN ResNet-50, 13 partitioning points for GoogLeNet, and 8 partitioning points for SqueezeNet V1.1 within 520 s, 330 s, and 140 s, respectively, on an AMD EPYC 7702P running 8 concurrent threads. For GoogLeNet, we determine two partitioning points that provide a good trade-off between required bandwidth, latency and energy consumption. We also provide insights into further interesting findings that can be derived from the evaluation results

    Approximate Computing for Energy Efficiency

    Get PDF

    Compiler-centric across-stack deep learning acceleration

    Get PDF
    Optimizing the deployment of Deep Neural Networks (DNNs) is hard. Despite deep learning approaches increasingly providing state-of-the-art solutions to a variety of difficult problems, such as computer vision and natural language processing, DNNs can be prohibitively expensive, for example, in terms of inference time or memory usage. Effective exploration of the design space requires a holistic approach, including a range of topics from machine learning, systems, and hardware. The rapid proliferation of deep learning applications has raised demand for efficient exploration and acceleration of deep learning based solutions. However, managing the range of optimization techniques, as well as how they interact with each other across the stack is a non-trivial task. A family of emerging specialized compilers for deep learning, tensor compilers, appear to be a strong candidate to help manage the complexity of across-stack optimization choices, and enable new approaches. This thesis presents new techniques and explorations of the Deep Learning Acceleration Stack (DLAS), with the perspective that the tensor compiler will increasingly be the center of this stack. First, we motivate the challenges in exploring DLAS, by describing the experience of running a perturbation study varying parameters at every layer of the stack. The core of the study is implemented using a tensor compiler, which reduces the complexity of evaluating the wide range of variants, although still requires a significant engineering effort to realize. Next, we develop a new algorithm for grouped convolution, a model optimization technique for which existing solutions provided poor inference time scaling. We implement and optimize our algorithm using a tensor compiler, outperforming existing approaches by 5.1× on average (arithmetic mean). Finally, we propose a technique, transfer-tuning, to reduce the search time required for automatic tensor compiler code optimization, reducing the search time required by 6.5× on average. The techniques and contributions of this thesis across these interconnected domains demonstrate the exciting potential of tensor compilers to simplify and improve design space exploration for DNNs, and their deployment. The outcomes of this thesis enable new lines of research to enable machine learning developers to keep up with the rapidly evolving landscape of neural architectures and hardware

    Feasibility Study of High-Level Synthesis : Implementation of a Real-Time HEVC Intra Encoder on FPGA

    Get PDF
    High-Level Synthesis (HLS) on automatisoitu suunnitteluprosessi, joka pyrkii parantamaan tuottavuutta perinteisiin suunnittelumenetelmiin verrattuna, nostamalla suunnittelun abstraktiota rekisterisiirtotasolta (RTL) käyttäytymistasolle. Erilaisia kaupallisia HLS-työkaluja on ollut markkinoilla aina 1990-luvulta lähtien, mutta vasta äskettäin ne ovat alkaneet saada hyväksyntää teollisuudessa sekä akateemisessa maailmassa. Hidas käyttöönottoaste on johtunut pääasiassa huonommasta tulosten laadusta (QoR) kuin mitä on ollut mahdollista tavanomaisilla laitteistokuvauskielillä (HDL). Uusimmat HLS-työkalusukupolvet ovat kuitenkin kaventaneet QoR-aukkoa huomattavasti. Tämä väitöskirja tutkii HLS:n soveltuvuutta videokoodekkien kehittämiseen. Se esittelee useita HLS-toteutuksia High Efficiency Video Coding (HEVC) -koodaukselle, joka on keskeinen mahdollistava tekniikka lukuisille nykyaikaisille mediasovelluksille. HEVC kaksinkertaistaa koodaustehokkuuden edeltäjäänsä Advanced Video Coding (AVC) -standardiin verrattuna, saavuttaen silti saman subjektiivisen visuaalisen laadun. Tämä tyypillisesti saavutetaan huomattavalla laskennallisella lisäkustannuksella. Siksi reaaliaikainen HEVC vaatii automatisoituja suunnittelumenetelmiä, joita voidaan käyttää rautatoteutus- (HW ) ja varmennustyön minimoimiseen. Tässä väitöskirjassa ehdotetaan HLS:n käyttöä koko enkooderin suunnitteluprosessissa. Dataintensiivisistä koodaustyökaluista, kuten intra-ennustus ja diskreetit muunnokset, myös enemmän kontrollia vaativiin kokonaisuuksiin, kuten entropiakoodaukseen. Avoimen lähdekoodin Kvazaar HEVC -enkooderin C-lähdekoodia hyödynnetään tässä työssä referenssinä HLS-suunnittelulle sekä toteutuksen varmentamisessa. Suorituskykytulokset saadaan ja raportoidaan ohjelmoitavalla porttimatriisilla (FPGA). Tämän väitöskirjan tärkein tuotos on HEVC intra enkooderin prototyyppi. Prototyyppi koostuu Nokia AirFrame Cloud Server palvelimesta, varustettuna kahdella 2.4 GHz:n 14-ytiminen Intel Xeon prosessorilla, sekä kahdesta Intel Arria 10 GX FPGA kiihdytinkortista, jotka voidaan kytkeä serveriin käyttäen joko peripheral component interconnect express (PCIe) liitäntää tai 40 gigabitin Ethernettiä. Prototyyppijärjestelmä saavuttaa reaaliaikaisen 4K enkoodausnopeuden, jopa 120 kuvaa sekunnissa. Lisäksi järjestelmän suorituskykyä on helppo skaalata paremmaksi lisäämällä järjestelmään käytännössä minkä tahansa määrän verkkoon kytkettäviä FPGA-kortteja. Monimutkaisen HEVC:n tehokas mallinnus ja sen monipuolisten ominaisuuksien mukauttaminen reaaliaikaiselle HW HEVC enkooderille ei ole triviaali tehtävä, koska HW-toteutukset ovat perinteisesti erittäin aikaa vieviä. Tämä väitöskirja osoittaa, että HLS:n avulla pystytään nopeuttamaan kehitysaikaa, tarjoamaan ennen näkemätöntä suunnittelun skaalautuvuutta, ja silti osoittamaan kilpailukykyisiä QoR-arvoja ja absoluuttista suorituskykyä verrattuna olemassa oleviin toteutuksiin.High-Level Synthesis (HLS) is an automated design process that seeks to improve productivity over traditional design methods by increasing design abstraction from register transfer level (RTL) to behavioural level. Various commercial HLS tools have been available on the market since the 1990s, but only recently they have started to gain adoption across industry and academia. The slow adoption rate has mainly stemmed from lower quality of results (QoR) than obtained with conventional hardware description languages (HDLs). However, the latest HLS tool generations have substantially narrowed the QoR gap. This thesis studies the feasibility of HLS in video codec development. It introduces several HLS implementations for High Efficiency Video Coding (HEVC) , that is the key enabling technology for numerous modern media applications. HEVC doubles the coding efficiency over its predecessor Advanced Video Coding (AVC) standard for the same subjective visual quality, but typically at the cost of considerably higher computational complexity. Therefore, real-time HEVC calls for automated design methodologies that can be used to minimize the HW implementation and verification effort. This thesis proposes to use HLS throughout the whole encoder design process. From data-intensive coding tools, like intra prediction and discrete transforms, to more control-oriented tools, such as entropy coding. The C source code of the open-source Kvazaar HEVC encoder serves as a design entry point for the HLS flow, and it is also utilized in design verification. The performance results are gathered with and reported for field programmable gate array (FPGA) . The main contribution of this thesis is an HEVC intra encoder prototype that is built on a Nokia AirFrame Cloud Server equipped with 2.4 GHz dual 14-core Intel Xeon processors and two Intel Arria 10 GX FPGA Development Kits, that can be connected to the server via peripheral component interconnect express (PCIe) generation 3 or 40 Gigabit Ethernet. The proof-of-concept system achieves real-time. 4K coding speed up to 120 fps, which can be further scaled up by adding practically any number of network-connected FPGA cards. Overcoming the complexity of HEVC and customizing its rich features for a real-time HEVC encoder implementation on hardware is not a trivial task, as hardware development has traditionally turned out to be very time-consuming. This thesis shows that HLS is able to boost the development time, provide previously unseen design scalability, and still result in competitive performance and QoR over state-of-the-art hardware implementations

    Design and management of image processing pipelines within CPS : Acquired experience towards the end of the FitOptiVis ECSEL Project

    Get PDF
    Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.Peer reviewe
    corecore