Compile Time Research Articles

COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images.

Simple SummaryThe study proposes an automated deep learning-based classification model, based on a Convolutional Neural Network, that demonstrates a rapid detection rate for COVID-19. The training dataset consists of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images which were then augmented. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models. MobileNetV2 showed enough promise to make it a candidate for further modification. The resulting model produced the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.

Efficient automatic scheduling of imaging and vision pipelines for the GPU

We present a new algorithm to quickly generate high-performance GPU implementations of complex imaging and vision pipelines, directly from high-level Halide algorithm code. It is fully automatic, requiring no schedule templates or hand-optimized kernels. We address the scalability challenge of extending search-based automatic scheduling to map large real-world programs to the deep hierarchies of memory and parallelism on GPU architectures in reasonable compile time. We achieve this using (1) a two-phase search algorithm that first ‘freezes’ decisions for the lowest cost sections of a program, allowing relatively more time to be spent on the important stages, (2) a hierarchical sampling strategy that groups schedules based on their structural similarity, then samples representatives to be evaluated, allowing us to explore a large space with few samples, and (3) memoization of repeated partial schedules, amortizing their cost over all their occurrences. We guide the process with an efficient cost model combining machine learning, program analysis, and GPU architecture knowledge. We evaluate our method’s performance on a diverse suite of real-world imaging and vision pipelines. Our scalability optimizations lead to average compile time speedups of 49x (up to 530x). We find schedules that are on average 1.7x faster than existing automatic solutions (up to 5x), and competitive with what the best human experts were able to achieve in an active effort to beat our automatic results.

Label dependent lambda calculus and gradual typing

Dependently-typed programming languages are gaining importance, because they can guarantee a wide range of properties at compile time. Their use in practice is often hampered because programmers have to provide very precise types. Gradual typing is a means to vary the level of typing precision between program fragments and to transition smoothly towards more precisely typed programs. The combination of gradual typing and dependent types seems promising to promote the widespread use of dependent types. We investigate a gradual version of a minimalist value-dependent lambda calculus. Compile-time calculations and thus dependencies are restricted to labels, drawn from a generic enumeration type. The calculus supports the usual Pi and Sigma types as well as singleton types and subtyping. It is sufficiently powerful to provide flexible encodings of variant and record types with first-class labels. We provide type checking algorithms for the underlying label-dependent lambda calculus and its gradual extension. The gradual type checker drives the translation into a cast calculus, which extends the original language. The cast calculus comes with several innovations: refined typing for casts in the presence of singletons, type reduction in casts, and fully dependent Sigma types. Besides standard metatheoretical results, we establish the gradual guarantee for the gradual language.

THE PROBLEMS OF HISTORY OF CREATION OF ALISHER NAVOI’S WORKS

This article is devoted to the history of creation of the works of Alisher Navoi. It considers the history of the collection “Hazoin ul - Maoniy” (“Тreasury of Thoughts”), the poem Saddi Iskandari (Alexander’s Wall) and the religious work Siroj ul-Muslimin (“The Light of Muslims”). The author pays special attention to such issues as the time of compilation of “Hazoin ul –Maoniy “(“Treasury of Thoughts”) and in what period of the poet’s life and conditions the poems were included in the collection. Conclusions obtained because of author’s research are set out in the end of the article. The article also clarified the issue related to the history of the creation of Siroj ul –Muslimin (“The Light of Muslims”).

817Official small area health statistics using the stratified reweighting method

Abstract Background The Australian Bureau of Statistics (ABS) presently produces health data for small population groups using a Generalised Linear Mixed Model (GLMM) method. Although this method is highly effective at producing reliable local level health data, it takes several months to compile data once it’s collected. The Stratified Reweighting Method (SRM) was investigated as an innovative efficient method for producing local level health data. Methods The SRM harnesses information from both health survey and Census data. A cluster analysis of 12 Census data items creates 13 area groups with similar population demographics. A replicated survey data set is then created where each small area is bolstered by the other small areas within its area group. The survey weights from this dataset are adjusted to match Census data of each small area across several demographic variables. A final survey weight adjustment ensures consistency of the small area predictions with national survey estimates. Results Health statistics were produced for over 20 health outcomes in the latest ABS National Health Survey; and the ABS Survey of Disability, Ageing and Carers. It was found that, compared to the GLMM method: the models had lower, but still acceptable quality; the errors of prevalence estimates were similar magnitude; and the data compilation time was reduced to within two weeks. Conclusions The SRM is an efficient approach for producing acceptable quality official local health statistics. Key messages The SRM is an innovative and efficient weight-based method using health survey and population Census data to produce official local health statistics.

Minimizing total absolute deviation of job completion times on a single machine with maintenance activities using a Lion Optimization Algorithm

Scheduling is a decision-making process that plays an important role in the service and production industries. Effective scheduling can assist companies to survive in the competitive market. Single machine scheduling is an important optimization problem in the scheduling research area. It can be found in a wide range of real-world engineering problems, from manufacturing to computer science. Due to the high complexity of single machine scheduling problems, developing approximation methods, particularly metaheuristic algorithms, for solving them have absorbed considerable attention. In this study, a Lion Optimization Algorithm (LOA) is employed to solve a single machine with maintenance activities, where the objective is to minimize the Total Absolute Deviation of Compilation Times (TADC). In the scheduling literature, TADC as an objective function has hardly been studied. To evaluate the performance of the LOA, it was compared against a set of well-known metaheuristics. Therefore, a set of problem was generated, and a comprehensive experimental analysis was conducted. The results of computational experiments indicate the superiority of the proposed optimization method.

HoloGen: An open-source toolbox for high-speed hologram generation

The rise of virtual and augmented reality systems has prompted an increase in interest in the fields of 2D and 3D computer-generated holography (CGH). The numerical processing required to generate a hologram is high and requires significant domain expertise. This has historically slowed the adoption of CGH in emerging fields.In this paper we introduce HoloGen, an open-source Cuda C and ▪ framework for computer-generated holography. HoloGen unites, for the first time, a wide array of existing hologram generation algorithms with state of the art performance while attempting to remain intuitive and easy to use. This is enabled by a ▪ and Windows Presentation Framework (WPF) graphical user interface (GUI). A novel reflection based parameter hierarchy is used to ensure ease of modification. Extensive use of ▪ templates based on the Standard Template Library (STL), compile time flexibility is preserved while maintaining runtime performance.The current release of HoloGen unites implementations of well known generation algorithms including Gerchberg-Saxton (GS), Liu-Taghizadeh (LT), direct search (DS), simulated annealing (SA) and one-step phase-retrieval (OSPR) with less known specialist variants including weighted GS and Adaptive OSPR.Benchmarking results are presented for several key algorithms. The software is freely available under an MIT license. Program summaryProgram title: HoloGen v2.2.1.17177CPC Library link to program files:https://doi.org/10.17632/nzk4swwsrg.1Developer's repository link:https://gitlab.com/CMMPEOpenAccess/HoloGenLicensing provisions: MITProgramming language: Cuda, ▪, ▪External packages: Cuda, ManagedCuda, MathNet, Newtonsoft.Json, NUnit, AForge, Accord, ClosedXML, CefSharp, PdfiumViewer, Xceed, NHotkey, SharpDX, MaterialSkin, Xamarin.forms, HelixToolkit, Dragablz, LiveCharts, MahAppsNature of problem: Hologram generation for two-dimensional Fourier and Fresnel holograms displayed on amplitude or phase modulating spatial light modulators with binary or multi-level controlSolution method: Algorithmic variants including Gerchberg-Saxton, Liu-Taghizadeh, Direct Search, Simulated Annealing and One-Step Phase-Retrieval. Includes real-time reporting, batch processing and complex field manipulationAdditional comments include restrictions and unusual features: Graphical user interface only exposes access to two-dimensional hologram generation. Includes a novel reflection based parameter hierarchy for ease of modification.

ElectroMicroTransport v2107: Open-source toolbox for paper-based electromigrative separations

electroMicroTransport v2107: Open-source toolbox for paper-based electromigrative separations

Treatment of Repeated Letdowns in Coordinated Consistent Recovery Line Compilation for Mobile Distributed Systems

We put forward a least_int_method (least interactive method) orchestrated CRL-compilation (consistent recovery line compilation) etiquette for non- deterministic Mob_DS (Mobile Distributed Systems); where no inoperable reinstatement-points are recorded. Recurrent terminations of CRL-compilation procedure may happen in Mobile_DS due to exhausted battery, non-voluntary disconnections of Mob_Nodes, or poor wireless connectivity. Therefore, we put forward that in the first stage, all pertinent Mob_Nodes will capture transient reinstatement-point only. Transient reinstatement-point is stored on the memory of Mob_Node only. In this case, if some method fails to capture its reinstatement-point in the first stage, then Mob_Nodes need to abandon their transient reinstatement-points only. In this way, we try to abate the loss of CRL-compilation effort when any method fails to capture its reinstatement-point in harmonization with others. We also try to reduce the CRL- compilation time and intrusion time of methods by limiting CRL-compilation tree which may be formed in other etiquettes [2, 9, 10]. We captured the transitive dependencies during the normal execution by piggybacking causal-dependency-vectors onto computation communications.

Symbolic Loop Compilation for Tightly Coupled Processor Arrays

Tightly Coupled Processor Arrays (TCPAs), a class of massively parallel loop accelerators, allow applications to offload computationally expensive loops for improved performance and energy efficiency. To achieve these two goals, executing a loop on a TCPA requires an efficient generation of specific programs as well as other configuration data for each distinct combination of loop bounds and number of available processing elements (PEs). Since both these parameters are generally unknown at compile time—the number of available PEs due to dynamic resource management, and the loop bounds, because they depend on the problem size—both the programs and configuration data must be generated at runtime. However, pure just-in-time compilation is impractical, because mapping a loop program onto a TCPA entails solving multiple NP-complete problems. As a solution, this article proposes a unique mixed static/dynamic approach called symbolic loop compilation. It is shown that at compile time, the NP-complete problems (modulo scheduling, register allocation, and routing) can still be solved to optimality in a symbolic way resulting in a so-called symbolic configuration , a space-efficient intermediate representation parameterized in the loop bounds and number of PEs. This phase is called symbolic mapping . At runtime, for each requested accelerated execution of a loop program with given loop bounds and known number of available PEs, a concrete configuration , including PE programs and configuration data for all other components, is generated from the symbolic configuration according to these parameter values. This phase is called instantiation . We describe both phases in detail and show that instantiation runs in polynomial time with its most complex step, program instantiation, not directly depending on the number of PEs and thus scaling to arbitrary sizes of TCPAs. To validate the efficiency of this mixed static/dynamic compilation approach, we apply symbolic loop compilation to a set of real-world loop programs from several domains, measuring both compilation time and space requirements. Our experiments confirm that a symbolic configuration is a space-efficient representation suited for systems with little memory—in many cases, a symbolic configuration is smaller than even a single concrete configuration instantiated from it—and that the times for the runtime phase of program instantiation and configuration loading are negligible and moreover independent of the size of the available processor array. To give an example, instantiating a configuration for a matrix-matrix multiplication benchmark takes equally long for 4× 4 and 32× 32 PEs.

Features of the Formation of Partes Singing Collections of the Late 17th — Mid 18th Centuries

The Synodal Singing Collection (SSC) of the State Historical Museum is one of the most extensive collections of singing manuscripts collected in the last quarter of the 19th — early 20th centuries from all over Russia in the Synodal School of Church Singing. It contains hooked notation manuscripts of various chants, notolinear manuscripts with harmonization of ancient chants, partes concerts and “Services of God” by composers of the 17th — 18th centuries. At present, the research focuses on the specifics of the musical techniques of Russian partes concerts, correlation of the verbal text and music, their repertoire, authorship, the fate of concert cycles, the arrangements for various number of singing voices, etc. However, the important issue of the time of compilation of concert collections has not received sufficient coverage in the scientific literature. The goal of this study is, firstly, to determine the specifics of the formation of such collections using the example of the manuscript No. 360/1-8 from the Synodal Singing Collection the State Historical Museum with the involvement of other manuscripts of this Collection. The detailed palaeographic analysis of eight manuscripts that made up the collection, which previously belonged to the Novgorod Bishop’s House, allowed the author to determine the time of recording of each concert, as well as the time of compilation of the collection. Analysis of the paper watermarks and the notes placed on the sheets of manuscripts showed that the collection No. 360/1-8 of SSC from the State Historical Museum is a convolute, which is based on the works rewritten in the 1720s with the inclusion of Services recorded in the early and mid 18th century. In addition, the cycle of stichera and slavniki from the Feast Services was written after the compilation and, probably, after binding the voice parts of the collection, possibly by its compiler. The study of the notes of singers placed in the parts of this collection allowed the author of the article to find out their names, time and specifics of their singing activities. The conclusions drawn from the example of the collection No. 360/1-8 of SSC of the State Historical Museum are applicable to many collections of that time.

PICO

Memory safety violations such as buffer overflows are a threat to security to this day. A common solution to ensure memory safety for C is code instrumentation. However, this often causes high execution-time overhead and is therefore rarely used in production. Static analyses can reduce this overhead by proving some memory accesses in bounds at compile time. In practice, however, static analyses may fail to verify in-bounds accesses due to over-approximation. Therefore, it is important to additionally optimize the checks that reside in the program. In this article, we present PICO, an approach to eliminate and replace in-bounds checks. PICO exactly captures the spatial memory safety of accesses using Presburger formulas to either verify them statically or substitute existing checks with more efficient ones. Thereby, PICO can generate checks of which each covers multiple accesses and place them at infrequently executed locations. We evaluate our LLVM-based PICO prototype with the well-known SoftBound instrumentation on SPEC benchmarks commonly used in related work. PICO reduces the execution-time overhead introduced by SoftBound by 36% on average (and the code-size overhead by 24%). Our evaluation shows that the impact of substituting checks dominates that of removing provably redundant checks.

Evaluation of automatic parallelization algorithms to minimize speculative parallelism overheads: An experiment

Automatic parallelization is a crucial objective of the parallel computing architecture that can be achieved through conversion of sequential code into multi-threaded code, which will run in parallel manner. This approach focuses largely on the loops since they take most of the execution time in programs. Thread level speculation techniques come into roleplay while checking for the dependencies. These dependencies cannot be identified at the compile time, thus providing a larger scope of parallelization when combined with other parallelisation techniques. This results in a greater speedup and more accurate parallel code formation. In this research paper, an experiment to evaluate the performance and comparative analysis has been done among key automatic parallelization algorithms on different parameters like number of cores, speedup and loop dependency taking into consideration of speculation.

Low-latency compilation of SQL queries to machine code

Query compilation has proven to be one of the most efficient query processing techniques. Despite its fast processing speed, the additional compilation times of the technique limit its applicability. This is because the approach is most beneficial only when the improvements in processing time clearly exceed the additional compilation time. Recently the feasibility of query compilers with very low compilation times has been shown. This may prove query compilation as a merely universal approach. In this article and in the corresponding live demo, we show the capabilities of the ReSQL database system, which uses the intermediate representation Flounder IR to achieve very low compilation times. ReSQL reduces the compilation times from SQL to machine code compared to existing LLVM-based techniques by up to 101.1x for real-world analytic queries.

Static Privacy Analysis by Flow Reconstruction of Tainted Data

Software security vulnerabilities and leakages of private information are two of the main issues in modern software systems. Several different approaches, ranging from design techniques to run-time monitoring, have been applied to prevent, detect and isolate such vulnerabilities. Static taint analysis has been particularly successful in detecting injection vulnerabilities at compile time. However, its extension to detect leakages of sensitive data has been only partially investigated. In this paper, we introduce BackFlow, a backward flow reconstructor that, starting from the results of a generic taint analysis engine, reconstructs the flow of tainted data. If successful, BackFlow provides full information about the flow that such data (e.g. private information or user input) traversed inside the program before reaching a sensitive point (e.g. Internet communication or execution of an SQL query). Such information is needed to extend taint analysis to privacy analyses, since in such a scenario it is important to know which exact type of sensitive data flows to what type of communication channels. BackFlow has been implemented in Julia (an industrial static analyzer for Java, Android and .NET programs), and applied to WebGoat and different benchmarks to detect both injections and privacy issues. The experimental results prove that BackFlow is able to reconstruct the flow of tainted data for most of the true positives, it scales up to industrial applications, and it can be effectively applied to privacy analysis, such as the detection of sensitive data leaks or compliance with a data regulation.

Physiotherapy approach in stroke rehabilitation: Development and testing of a survey of current practice

BACKGROUND AND PURPOSE: Physiotherapy is a key discipline in stroke rehabilitation. Physiotherapists sometimes select interventions following personal preference rather than a scientific rational. Data on physiotherapy approaches used in stroke rehabilitation may help policy makers and educators to plan education strategies and implement efficient clinical practices, thus improving rehabilitation effectiveness. We aimed to develop and test a questionnaire designed to survey physiotherapy interventions utilized in stroke rehabilitation. METHODS: We used a multistep questionnaire development method (literature review and synthesis; questionnaire drafting; expert validation; cognitive interviewing). Afterwards the survey proceeded to reliability testing; the outcomes of interest were completion time, estimated comprehensiveness of the lists of interventions, and intraclass correlation coefficient (ICC). RESULTS AND DISCUSSION: Retrieved survey tools, their items, and a taxonomy were useful for the questionnaire development. We interviewed four experts; changes were made to the survey following their suggestions. Thirteen physiotherapists participated in cognitive interviewing and further changes were made. Thirty-five raters participated in the test-retest study. Most participants considered the list of physiotherapy interventions and assistive device exhaustive. Median compilation time and time interval between the two compilations were 7 minutes and 21 days. The observed ICC was 0.844 (95% confidence interval, 0.829/0.857). Our method provided a valid and reliable questionnaire, however further methodological considerations of sampling methods and contact delivery modes are needed. CONCLUSIONS: If adequately implemented, the questionnaire can provide information about interventions utilized in stroke rehabilitation practice by physiotherapists. Data eventually acquired could be useful for planning educational strategies and implementing effective clinical practices.

Tidy Tuples and Flying Start: fast compilation and fast execution of relational queries in Umbra

Although compiling queries to efficient machine code has become a common approach for query execution, a number of newly created database system projects still refrain from using compilation. It is sometimes claimed that the intricacies of code generation make compilation-based engines too complex. Also, a major barrier for adoption, especially for interactive ad hoc queries, is long compilation time. In this paper, we examine all stages of compiling query execution engines and show how to reduce compilation overhead. We incorporate the lessons learned from a decade of generating code in HyPer into a design that manages complexity and yields high speed. First, we introduce a code generation framework that establishes abstractions to manage complexity, yet generates code in a single fast pass. Second, we present a program representation whose data structures are tuned to support fast code generation and compilation. Third, we introduce a new compiler backend that is optimized for minimal compile time, and simultaneously, yields superior execution performance to competing approaches, e.g., Volcano-style or bytecode interpretation. We implemented these optimizations in our database system Umbra to show that it is possible to unite fast compilation and fast execution. Indeed, Umbra achieves unprecedentedly low query latencies. On small data sets, it is even faster than interpreter engines like DuckDB and PostgreSQL. At the same time, on large data sets, its throughput is on par with the state-of-the-art compiling system HyPer.

Optimization with the OpenACC-to-FPGA framework on the Arria 10 and Stratix 10 FPGAs

The reconfigurable computing paradigm with field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs’ unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. We perform this exploration on Intel Arria 10 and Stratix 10 FPGAs. We also explored the relationships between performance, resource usages, and compilation times, and investigated implications for performance portability. Finally, we present an initial evaluation of a real-world proxy application, LULESH.

Laboratories of Design: A Catalog of Policy Innovation Labs in Europe

To address both persistent and emerging social and environmental problems, governments around the world have been seeking innovative ways to generate policy solutions in collaboration with citizens. One prominent trend during recent decades is the proliferation of Policy Innovation Labs (PILs), in which the search for policy solutions is embedded within scientific laboratory-like structures. Spread across the public, private, and non-profit sectors, and often funded by local, regional, or national governments, PILs utilize experimental methods, testing, and measurement to generate innovative, evidence-based policy solutions to complex public issues. This catalog lists PILs in Europe. For each lab, a one-page profile specifies its vision, policy innovation approaches, methodologies, major projects, parent entity, funding sources, and its alignment with the United Nations Sustainable Development Goals (SDG) call to action. For each lab we identify governmental, municipal, multi-sectorial, academic, non-profit, or private sector affiliation. The goals of compiling this catalog and making it available to citizens, scholars, NGOs, and public officials are to call attention to the growing spirit of citizen engagement in developing innovative policy solutions for their own communities and to facilitate collaboration and cross-pollination of ideas between organizations. Despite their increasing importance in public policy making, PILs are as yet understudied. This catalog will provide an opportunity for scholars to explore the function and value of community-oriented policy innovation as well as the effects of approaching policy making around disruptive social problems in a “scientific” way. Methodology: This catalog of policy innovation labs was compiled based on published reports, as well as a Google search for each individual country using the terms “policy lab” and “innovation lab,” first in English, then in the native language. Sometimes the labs themselves came up in the search results; for others, an article or a blog that mentioned them appeared. Next, each lab was searched specifically by name or by using an identified link. Each lab website that was identified was searched for other labs that were mentioned. Some labs were identified more than once, and a few that were found to be defunct or lacking a website were excluded. Innovation labs that referred only to technical or technological innovations were omitted. Only labs that relate to policy and to so-called “public innovation” were included in this catalog. Eligible PILs could be run and/or sponsored by local, regional, or national governments, universities, non-profit organizations, or the private sector. This resulted in a total of 212 European PILs. Notably, while the global proliferation of policy innovation labs is acknowledged by formal, global organizations, there are no clear-cut criteria to determine which organizations are considered PILs. Therefore, this catalog follows the precedent set by previous catalogs and identifies PILs as organizations that generate policy recommendations for social problems and public issues by employing a user-oriented design approach and utilizing experimental methods. Information about every lab was collected form its website, with minimal editing for coherence. For some labs, information was presented in English on its website; for others, information in the native language was translated into English using machine translation followed by human editing. Data for the catalog was collected between December 2019 and July 2020. PILs are opening and closing with increasing frequency so this catalog serves as a snapshot in time, featuring PILs that are currently active as of the time of compilation. Alignment with the UNDP’s 17 Sustainable Development goals was determined based on each lab’s policy areas of focus. These goals are “No Poverty, Zero Hunger, Good Health and Well-being, Quality Education, Gender Equality, Clean Water and Sanitation, Affordable and Clean Energy, Decent Work and Economic Growth, Industry, Innovation and Infrastructure, Reduced Inequality, Sustainable Cities and Communities, Responsible Consumption and Production, Climate Action, Life Below Water, Life on Land, Peace and Justice Strong Institutions, Partnerships to achieve the Goal.” The first to specify European PILs’ compatibility with SDG, this catalog follows in the footsteps of PIL catalogs created for the European Union, Canada and the United States, adding to a valuable and growing body of knowledge about human- and design-centred entrepreneurial approaches to policy making. This report is part of a larger BSF sponsored project examining PILs in OECD countries.

VR-ZYCAP: A Versatile Resourse-Level ICAP Controller for ZYNQ SOC

Hybrid architectures integrating a processor with an SRAM-based FPGA fabric—for example, Xilinx ZynQ SoC—are increasingly being used as a single-chip solution in several market segments to replace multi-chip designs. These devices not only provide advantages in terms of logic density, cost and integration, but also provide run-time in-field reconfiguration capabilities. However, the current reconfiguration capabilities provided by vendor tools are limited to the module level. Therefore, incremental run-time configuration memory changes require a lengthy compilation time for off-line bitstream generation along with storage and reconfiguration time overheads with traditional vendor methodologies. In this paper, an internal configuration access port (ICAP) controller that provides a versatile fine-grain resource-level incremental reconfiguration of the programmable logic (PL) resources in ZynQ SoC is presented. The proposed controller implemented in PL, called VR-ZyCAP, can reconfigure look-up tables (LUTs) and Flip-Flops (FF). The run-time reconfiguration of FF is achieved through a reset after reconfiguration (RAR)-featured partial bitstream to avoid the unintended state corruption of other memory elements. Along with versatility, our proposed controller improves the reconfiguration time by 30 times for FFs compared to state-of-the-art works while achieving a nearly 400-fold increase in speed for LUTs when compared to vendor-supported software approaches. In addition, it achieves competitive resource utilization when compared to existing approaches.