79,178 research outputs found
Parasol: Efficient Parallel Synthesis of Large Model Spaces
Formal analysis is an invaluable tool for software engineers, yet state-of-the-art formal analysis techniques suffer from well-known limitations in terms of scalability. In particular, some software design domains—such as tradeoff analysis and security analysis—require systematic exploration of potentially huge model spaces, which further exacerbates the problem. Despite this present and urgent challenge, few techniques exist to support the systematic exploration of large model spaces. This paper introduces Parasol, an approach and accompanying tool suite, to improve the scalability of large-scale formal model space exploration. Parasol presents a novel parallel model space synthesis approach, backed with unsupervised learning to automatically derive domain knowledge, guiding a balanced partitioning of the model space. This allows Parasol to synthesize the models in each partition in parallel, significantly reducing synthesis time and making large-scale systematic model space exploration for real-world systems more tractable. Our empirical results corroborate that Parasol substantially reduces (by 460% on average) the time required for model space synthesis, compared to state-of-the-art model space synthesis techniques relying on both incremental and parallel constraint solving technologies as well as competing, non-learning-based partitioning methods
Synthesis of all-digital delay lines
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe synthesis of delay lines (DLs) is a core task during the generation of matched delays, ring oscillator clocks or delay monitors. The main figure of merit of a DL is the fidelity to track variability. Unfortunately, complex systems have a great diversity of timing paths that exhibit different sensitivities to static and dynamic variations. Designing DLs that capture this diversity is an ardous task. This paper proposes an algorithmic approach for the synthesis of DLs that can be integrated in a conventional design flow. The algorithm uses heuristics to perform a combinatorial search in a vast space of solutions that combine different types of gates and wire lengths. The synthesized DLs are (1) all digital, i.e., built of conventional standard cells, (2) accurate in tracking variability and (3) configurable at runtime. Experimental results with a commercial standard cell library confirm the quality of the DLs that only exhibit delay mismatches of about 1% on average over all PVT corners.Peer ReviewedPostprint (author's final draft
A Methodology for Efficient Space-Time Adapter Design Space Exploration: A Case Study of an Ultra Wide Band Interleaver
This paper presents a solution to efficiently explore the design space of
communication adapters. In most digital signal processing (DSP) applications,
the overall architecture of the system is significantly affected by
communication architecture, so the designers need specifically optimized
adapters. By explicitly modeling these communications within an effective
graph-theoretic model and analysis framework, we automatically generate an
optimized architecture, named Space-Time AdapteR (STAR). Our design flow inputs
a C description of Input/Output data scheduling, and user requirements
(throughput, latency, parallelism...), and formalizes communication constraints
through a Resource Constraints Graph (RCG). The RCG properties enable an
efficient architecture space exploration in order to synthesize a STAR
component. The proposed approach has been tested to design an industrial data
mixing block example: an Ultra-Wideband interleaver.Comment: ISBN:1-4244-0921-
Exact and heuristic allocation of multi-kernel applications to multi-FPGA platforms
FPGA-based accelerators demonstrated high energy efficiency compared to GPUs and CPUs. However, single FPGA designs may not achieve sufficient task parallelism. In this work, we optimize the mapping of high-performance multi-kernel applications, like Convolutional Neural Networks, to multi-FPGA platforms. First, we formulate the system level optimization problem, choosing within a huge design space the parallelism and number of compute units for each kernel in the pipeline. Then we solve it using a combination of Geometric Programming, producing the optimum performance solution given resource and DRAM bandwidth constraints, and a heuristic allocator of the compute units on the FPGA cluster.Peer ReviewedPostprint (author's final draft
A Survey of Symbolic Execution Techniques
Many security and software testing applications require checking whether
certain properties of a program hold for any possible usage scenario. For
instance, a tool for identifying software vulnerabilities may need to rule out
the existence of any backdoor to bypass a program's authentication. One
approach would be to test the program using different, possibly random inputs.
As the backdoor may only be hit for very specific program workloads, automated
exploration of the space of possible inputs is of the essence. Symbolic
execution provides an elegant solution to the problem, by systematically
exploring many possible execution paths at the same time without necessarily
requiring concrete inputs. Rather than taking on fully specified input values,
the technique abstractly represents them as symbols, resorting to constraint
solvers to construct actual instances that would cause property violations.
Symbolic execution has been incubated in dozens of tools developed over the
last four decades, leading to major practical breakthroughs in a number of
prominent software reliability applications. The goal of this survey is to
provide an overview of the main ideas, challenges, and solutions developed in
the area, distilling them for a broad audience.
The present survey has been accepted for publication at ACM Computing
Surveys. If you are considering citing this survey, we would appreciate if you
could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing
this survey, we would appreciate if you could use the following BibTeX entry:
http://goo.gl/Hf5Fv
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Design automation with the characteristics properties model and the property driven design for redesign
This paper presents a framework consisting of a mathematical model and an algorithm for representation, analysis and exploration of the design space in redesign problems. The framework develops and extends the existing formalism of the Characteristics Properties Model (CPM) and Property Driven Design (PDD). A platform independent quantitative model based on formal log-ic is presented to map the characteristics and properties, as well as the relations and dependencies between them, along with solution conditions. The model is based on generalization of existing mathematical design models and is support-ed by the development of an algorithm enabling property driven design. The re-sulting framework offers a rich and flexible syntax and vocabulary along with a mathematical and computational tool applicable to mechanical product design
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