01771nas a2200157 4500008004100000245007400041210006900115260001200184300001300196490000700209520129700216100002201513700002301535700001901558856003601577 2019 eng d00aAutomated Model-based Optimization of Data-Adaptable Embedded Systems0 aAutomated Modelbased Optimization of DataAdaptable Embedded Syst c02/2020 a22 pages0 v193 a
This paper presents a modeling and optimization framework that enables developers to model an application's data sources, tasks, and exchanged data tokens; specify application requirements through high-level design metrics and fuzzy logic based optimization rules; and define an estimation framework to automatically optimize the application at runtime. We demonstrate the modeling and optimization process via an example application for video-based vehicle tracking and collision avoidance. We analyze the benefits of runtime optimization by comparing the performance of static point solutions to dynamic solutions over five distinct execution scenarios, showing improvements of up to 74% for dynamic over static configurations.
1 aLizarraga, Adrian1 aSprinkle, Jonathan1 aLysecky, Roman uhttps://doi.org/10.1145/337214201253nas a2200241 4500008004100000245008200041210006900123520049300192653002800685653002100713100001900734700001300753700002200766700002100788700003100809700001900840700001800859700001900877700002300896700002200919700002300941856004700964 2019 eng d00aWiP Abstract: String stability of commercial adaptive cruise control vehicles0 aWiP Abstract String stability of commercial adaptive cruise cont3 aIn this work, we conduct a series of car-following experiments with seven different ACC vehicles and use the collected data to model the car-following behavior of each vehicle. Using a linear stability analysis, the string stability of each tested vehicle is analyzed. Addition- ally, platoon experiments with platoons of up to eight identical vehicles are conducted to validate the stability findings. Previously, only one commercial ACC system has been evaluated for string stability. 10aAdaptive Cruise Control10aString Stability1 aGunter, George1 aYang, Y.1 aStern, Raphael, E1 aWork, Daniel, B.1 aMonache, Maria, Laura Dell1 aBhadani, Rahul1 aBunting, Matt1 aLysecky, Roman1 aSprinkle, Jonathan1 aSeibold, Benjamin1 aPiccoli, Benedetto uhttps://dl.acm.org/citation.cfm?id=331332502032nas a2200277 4500008004100000022001400041245005800055210005700113300001900170490000700189520121400196653002201410653003101432653002301463653003401486100002101520700001801541700001901559700002901578700002701607700002301634700001601657700002301673700001901696856003901715 2017 eng d a1539-908700aTask Transition Scheduling for Data-Adaptable Systems0 aTask Transition Scheduling for DataAdaptable Systems a105:1–105:280 v163 aData-adaptable embedded systems operate on a variety of data streams, which requires a large degree of configurability and adaptability to support runtime changes in data stream inputs. Data-adaptable reconfigurable embedded systems, when decomposed into a series of tasks, enable a flexible runtime implementation in which a system can transition the execution of certain tasks between hardware and software while simultaneously continuing to process data during the transition. Efficient runtime scheduling of task transitions is needed to optimize system throughput and latency of the reconfiguration and transition periods. In this article, we provide an overview of a runtime framework enabling the efficient transition of tasks between software and hardware in response to changes in system inputs. We further present and analyze several runtime transition scheduling algorithms and highlight the latency and throughput tradeoffs for two data-adaptable systems. To evaluate the task transition selection algorithms, a case study was performed on an adaptable JPEG2000 implementation as well as three other synchronous dataflow systems characterized by transition latency and communication load.
10aData adaptability10ahardware/software codesign10amodel-based design10aruntime transition scheduling1 aSandoval, Nathan1 aMackin, Casey1 aWhitsitt, Sean1 aGopinath, Vijay, Shankar1 aMahadevan, Sachidanand1 aMilakovich, Andrew1 aMerry, Kyle1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://doi.acm.org/10.1145/304749800564nas a2200133 4500008004100000245010100041210006900142260001700211300001200228100002200240700001900262700002300281856012600304 2016 eng d00aModel-based Fuzzy Logic Classifier Synthesis for Optimization of Data-Adaptable Embedded Systems0 aModelbased Fuzzy Logic Classifier Synthesis for Optimization of aHartford, CT a293-3021 aLizarraga, Adrian1 aLysecky, Roman1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/model-based-fuzzy-logic-classifier-synthesis-optimization-data-adaptable-embedded-systems00460nas a2200121 4500008004100000245006500041210006300106260000900169100002200178700001900200700002300219856009600242 2016 eng d00aModel-Driven Optimization of Data-Adaptable Embedded Systems0 aModelDriven Optimization of DataAdaptable Embedded Systems bIEEE1 aLizarraga, Adrian1 aLysecky, Roman1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/model-driven-optimization-data-adaptable-embedded-systems-001989nas a2200145 4500008004100000245006500041210006300106260000900169300001200178520154200190100002201732700001901754700002301773856004701796 2016 eng d00aModel-driven Optimization of Data-Adaptable Embedded Systems0 aModeldriven Optimization of DataAdaptable Embedded Systems bIEEE a293-3023 aComplex sensing and decision applications such as object tracking and classification, video surveillance, unmanned aerial vehicle flight decisions, and others operate on vast data streams with dynamic characteristics. As the availability and quality of the sensed data changes, the underlying models and decision algorithms should continually adapt in order to meet desired high-level requirements. Due to the complexity of such dynamic data-driven systems, traditional design time techniques are often incapable of producing a solution that remains optimal in the face of dynamically changing data, algorithms, and even availability of computational resources. To assist developers of these systems, we present a modeling and optimization methodology that enables developers to capture application task flows and data sources, define associated quality metrics with data types, specify each algorithm’s data and quality requirements, and define a data quality estimation framework to optimize the application at runtime. We demonstrate each facet of the modeling and optimization process via a video-based vehicle tracking and collision avoidance application, and show how such an approach results in efficient design space exploration when selecting the optimal set of algorithm modalities. When searching for an application configuration within 1% to 5% of optimal, our model-guided approach can achieve speedups of up to 9.3X versus a standard genetic algorithm and speedups of up to 80X relative to a brute force algorithm.
1 aLizarraga, Adrian1 aLysecky, Roman1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/COMPSAC.2016.15601888nas a2200145 4500008004100000245010500041210006900146260000900215300001000224520140100234100001901635700002301654700001901677856004601696 2014 eng d00aGenerating Model Transformations for Mending Dynamic Constraint Violations in Cyber Physical Systems0 aGenerating Model Transformations for Mending Dynamic Constraint c2014 a35-403 aCyber physical systems by definition involve design constraints addressing the computation and communication necessary to control physical systems. These systems have been modeled using domain specific modeling languages, but some limitations exist in the continued application of such a modeling approach to more complex, or safety-critical, systems. Specifically, it is well known how to formulate constraints in a domain-specific modeling language in order to prevent users from building invalid structures, but existing constraint-based techniques do not take into consideration design requirements that may require analysis in the physical domain (i.e. dynamic constraints). Those analysis results, when interpreted by a domain expert, can inform changes to the model: unfortunately, this process does not scale. This paper presents an approach to integrating dynamic constraints that cannot be enforced using structural model constraints. The technique uses expert blocks to analyze systems and generates model transformations specific to the system using the results of those analyses to fix constraint violations. The paper describes a Dynamic Constraint Feedback (DCF) methodology for integrating this technique into existing systems from a generic perspective. Specific examples in this paper are derived from the domain of data adaptable reconfigurable embedded systems (DARES).
1 aWhitsitt, Sean1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://dx.doi.org/10.1145/2688447.268845400541nas a2200145 4500008004100000245009900041210006900140260003700209100001900246700002100265700001900286700001800305700002300323856004900346 2013 eng d00aEfficient Reconfiguration Methods to Enable Rapid Deployment of Runtime Reconfigurable Systems0 aEfficient Reconfiguration Methods to Enable Rapid Deployment of aPacific Grove, CAbIEEEc11/20131 aLysecky, Roman1 aSandoval, Nathan1 aWhitsitt, Sean1 aMackin, Casey1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ACSSC.2013.681040101315nas a2200421 4500008004100000245009200041210006900133260001000202300001200212653001700224653001500241653001300256653001400269653001600283653001700299653002200316653002100338653001300359653001900372653003100391653003200422653002300454653001500477653002500492653002800517653003600545653002200581653001900603653001800622653001900640653002100659653001400680100002100694700001800715700001900733700002300752856011800775 2013 eng d00aHow You Can Learn to Stop Worrying and Love Reconfigurable Embedded Systems: A Tutorial0 aHow You Can Learn to Stop Worrying and Love Reconfigurable Embed cApril a213-21410aC++ language10aC/C++ code10acodesign10aComputers10aConferences10adata streams10aembedded hardware10aembedded systems10aHardware10ahardware tasks10ahardware-software codesign10aimage processing algorithms10aJPEG2000 standards10amiddleware10amiddleware framework10amodeling infrastructure10areconfigurable embedded systems10aruntime behaviors10asoftware tasks10asoftware tool10asoftware tools10aTransform coding10aTutorials1 aSandoval, Nathan1 aMackin, Casey1 aLysecky, Roman1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/how-you-can-learn-stop-worrying-and-love-reconfigurable-embedded-systems-tutorial00397nas a2200121 4500008004100000245006000041210006000101300001000161100001900171700002300190700001900213856004300232 2013 eng d00aModel Based Development with the Skeleton Design Method0 aModel Based Development with the Skeleton Design Method a12-191 aWhitsitt, Sean1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://dx.doi.org/10.1109/ECBS.2013.1600511nas a2200145 4500008004100000245009600041210006900137300001200206100002100218700001800239700001900257700001900276700002300295856004700318 2013 eng d00aRuntime Hardware/Software Task Transition Scheduling for Runtime-Adaptable Embedded Systems0 aRuntime HardwareSoftware Task Transition Scheduling for RuntimeA a342-3451 aSandoval, Nathan1 aMackin, Casey1 aWhitsitt, Sean1 aLysecky, Roman1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/FPT.2013.671838201894nas a2200529 4500008004100000245015900041210006900200260001000269300001000279653002800289653002200317653003800339653003600377653002400413653001800437653001600455653002900471653002900500653004500529653002100574653003500595653001600630653001100646653001300657653002600670653003100696653004400727653003100771653004700802653002600849653003300875653001500908653002300923653003400946653002400980653001201004653003701016653002001053653003301073653003501106100002101141700001801162700001901180700001901199700002301218856012301241 2013 eng d00aSystem Throughput Optimization and Runtime Communication Middleware Supporting Dynamic Software-Hardware Task Migration in Data Adaptable Embedded Systems0 aSystem Throughput Optimization and Runtime Communication Middlew cApril a59-6810acombinatorial explosion10aData adaptability10adata adaptable design methodology10adata adaptable embedded systems10adata configurations10adata handling10aData models10adata profile correlation10adesign time optimization10adynamic software-hardware task migration10aembedded systems10aField programmable gate arrays10aFIFO queues10aFiring10aHardware10ahardware accelerators10ahardware-software codesign10ahardware-software communication wrapper10ahardware/software codesign10ahardware/software communication middleware10aheuristic programming10aheuristic search methodology10amiddleware10amodel-based design10aPareto optimal configurations10aPareto optimisation10aRuntime10aruntime communication middleware10asearch problems10asimulation-based methodology10asystem throughput optimization1 aSandoval, Nathan1 aMackin, Casey1 aWhitsitt, Sean1 aLysecky, Roman1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/system-throughput-optimization-and-runtime-communication-middleware-supporting-dynamic01296nas a2200181 4500008004100000245011100041210006900152300001000221520067900231653002200910653003100932653002300963100002300986700002001009700001901029700002301048856004301071 2012 eng d00aAutomated Software Generation and Hardware Coprocessor Synthesis for Data-Adaptable Reconfigurable Systems0 aAutomated Software Generation and Hardware Coprocessor Synthesis a15-233 aWe present an overview of a data-adaptable reconfigurable embedded systems design methodology. The paper presents a novel paradigm for hardware/software code sign and reconfigurable computing driven by data-adaptability. The data-adaptable approach allows designers to directly model the data configurability of the target application, thereby enabling a solution that permits dynamic reconfiguration based on the data profile of the incoming data stream. This approach permits low-power, small form-factor hardware implementations of algorithms that might otherwise consume significant resources, or perhaps exceed the available space of the reconfigurable hardware.
10aData adaptability10ahardware/software codesign10amodel-based design1 aMilakovich, Andrew1 aGopinath, Vijay1 aLysecky, Roman1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ECBS.2012.1600443nas a2200133 4500008004100000245007200041210006900113260001200182300000800194100001900202700002300221700001900244856004600263 2012 eng d00aAn Overseer Control Methodology for Data Adaptable Embedded Systems0 aOverseer Control Methodology for Data Adaptable Embedded Systems c08/2012 a1-61 aWhitsitt, Sean1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://dx.doi.org/10.1145/2508443.250844801419nas a2200181 4500008004100000245008300041210006900124260003200193300001000225520082600235100002701061700002001088700001901108700002301127700002101150700002301171856004301194 2011 eng d00aHardware/Software Communication Middleware for Data Adaptable Embedded Systems0 aHardwareSoftware Communication Middleware for Data Adaptable Emb bIEEE Computer Society Press a34-433 aRecent trends toward increased flexibility and configurability in emerging applications present demanding challenges for implementing systems that incorporate such capabilities. The resulting application configuration space is generally much larger than any one hardware implementation can support. We provide an overview of a new data-adaptive approach to the rapid design and implementation of such highly configurable applications. In support of this data-adaptable approach, we present and detail an efficient and flexible hardware/software communication middleware to support the seamless communication between hardware and software tasks at runtime. We highlight the flexibility of this interface and present an initial case study and results demonstrating the performance capabilities and area requirements.
1 aMahadevan, Sachidanand1 aGopinath, Vijay1 aLysecky, Roman1 aSprinkle, Jonathan1 aRozenblit, Jerzy1 aMarcellin, Michael uhttp://dx.doi.org/10.1109/ECBS.2011.1201646nas a2200145 4500008004100000245006300041210006300104260001000167300001200177520120600189100002001395700002301415700001901438856004301457 2011 eng d00aModeling of Data Adaptable Reconfigurable Embedded Systems0 aModeling of Data Adaptable Reconfigurable Embedded Systems cApril a276-2853 aMany applications require high flexibility, high configurability and high processing speeds. The physical constraints of a highly flexible system’s hardware implementation preclude a hardware solution that satisfies all configuration options. Similarly for pure software implementations, even if configurability is satisfied, process efficiency will be sacrificed. Thus for applications of any significant size, there can be no single hardware or software configuration that can efficiently support all the configurability options of the applications. The Data-Adaptable Reconfigurable Embedded System (DARES) approach tackles this problem through combination of the hardware-software co-design and reconfigurable computing methodologies. Data-adaptability means that as data streams change, the system is reconfigured along the baselines defined within the system’s specifications. In this project we use the concepts of Model-Integrated Computing to implement a domain-specific modeling language for the DARES approach. The language captures all the configurability options of the application task(s), performs design-space exploration, and provides a template for source code generation.
1 aGopinath, Vijay1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://dx.doi.org/10.1109/ECBS.2011.31