5,699 research outputs found
Introduction to the Special Issue on Automatic Program Generation for Embedded Systems
Domènech Montaner, LluĂsPla general d'edifici plurifamiliar.Planta baixa
principal i quatre plantes pis.Els baixos es
resolen amb un seguit d'arcs sobre
columnes de capitells florals, utilitzant
marbres de color rosa i blanc. Modernista,
1908-11
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
The SANDRA project: cooperative architecture/compiler technology for embedded real-time streaming applications
The convergence of digital television, Internet access, gaming, and digital media capture and playback stresses the importance of high-quality and high-performance video and graphics processing. The SANDRA project, a collaboration between Philips Research and INRIA, develops a consistent and efficient system design approach for regular, real-time constrained stream processing. The project aims at providing a system template with its associated compiler chain and application development framework, enabling an early validation of both the functional and the non-functional requirements of the application at every system design stage
DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines
Integrated data analysis (IDA) pipelines—that combine data management (DM) and query processing, high-performance computing
(HPC), and machine learning (ML) training and scoring—become
increasingly common in practice. Interestingly, systems of these
areas share many compilation and runtime techniques, and the
used—increasingly heterogeneous—hardware infrastructure converges as well. Yet, the programming paradigms, cluster resource
management, data formats and representations, as well as execution
strategies differ substantially. DAPHNE is an open and extensible
system infrastructure for such IDA pipelines, including language abstractions, compilation and runtime techniques, multi-level scheduling, hardware (HW) accelerators, and computational storage for
increasing productivity and eliminating unnecessary overheads. In
this paper, we make a case for IDA pipelines, describe the overall
DAPHNE system architecture, its key components, and the design
of a vectorized execution engine for computational storage, HW
accelerators, as well as local and distributed operations. Preliminary experiments that compare DAPHNE with MonetDB, Pandas,
DuckDB, and TensorFlow show promising results
Decoupling algorithms from schedules for easy optimization of image processing pipelines
Using existing programming tools, writing high-performance image processing code requires sacrificing readability, portability, and modularity. We argue that this is a consequence of conflating what computations define the algorithm, with decisions about storage and the order of computation. We refer to these latter two concerns as the schedule, including choices of tiling, fusion, recomputation vs. storage, vectorization, and parallelism.
We propose a representation for feed-forward imaging pipelines that separates the algorithm from its schedule, enabling high-performance without sacrificing code clarity. This decoupling simplifies the algorithm specification: images and intermediate buffers become functions over an infinite integer domain, with no explicit storage or boundary conditions. Imaging pipelines are compositions of functions. Programmers separately specify scheduling strategies for the various functions composing the algorithm, which allows them to efficiently explore different optimizations without changing the algorithmic code.
We demonstrate the power of this representation by expressing a range of recent image processing applications in an embedded domain specific language called Halide, and compiling them for ARM, x86, and GPUs. Our compiler targets SIMD units, multiple cores, and complex memory hierarchies. We demonstrate that it can handle algorithms such as a camera raw pipeline, the bilateral grid, fast local Laplacian filtering, and image segmentation. The algorithms expressed in our language are both shorter and faster than state-of-the-art implementations.National Science Foundation (U.S.) (Grant 0964004)National Science Foundation (U.S.) (Grant 0964218)National Science Foundation (U.S.) (Grant 0832997)United States. Dept. of Energy (Award DE-SC0005288)Cognex CorporationAdobe System
Formal Verification of Industrial Software and Neural Networks
Software ist ein wichtiger Bestandteil unsere heutige Gesellschaft. Da Software vermehrt
in sicherheitskritischen Bereichen angewandt wird, mĂĽssen wir uns auf eine korrekte und
sichere Ausführung verlassen können. Besonders eingebettete Software, zum Beispiel in
medizinischen Geräten, Autos oder Flugzeugen, muss gründlich und formal geprüft werden.
Die Software solcher eingebetteten Systeme kann man in zwei Komponenten aufgeteilt.
In klassische (deterministische) Steuerungssoftware und maschinelle Lernverfahren
zum Beispiel fĂĽr die Bilderkennung oder Kollisionsvermeidung angewandt werden.
Das Ziel dieser Dissertation ist es den Stand der Technik bei der Verifikation von
zwei Hauptkomponenten moderner eingebetteter Systeme zu verbessern: in C/C++
geschriebene Software und neuronalen Netze. FĂĽr beide Komponenten wird das Verifikationsproblem
formal definiert und neue Verifikationsansätze werden vorgestellt
- …