Slide 1Slide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Alternative Parallel Alternative Parallel Processing ApproachesProcessing ApproachesJonathan SagabaenJonathan SagabaenOverviewOverviewNeural NetworksNeural NetworksSystolic ArraysSystolic ArraysDataflow ComputingDataflow ComputingDataflow ComputingDataflow ComputingData flows Data flows continuously and is continuously and is available to multiple available to multiple instructions at the instructions at the same timesame timeEach instruction is Each instruction is considered to be a considered to be a separate processseparate processAn instruction is An instruction is executed when the executed when the data necessary for data necessary for execution become execution become available available Example >>Example >>Instructions do not Instructions do not reference memory; reference memory; instead, they instead, they reference other reference other instructionsinstructionsDataflow ComputingDataflow ComputingUpdating spreadsheetUpdating spreadsheetIn a spreadsheet you In a spreadsheet you can specify a cell can specify a cell formula which formula which depends on other depends on other cells cells Then when any of Then when any of those cells is those cells is updated the first updated the first cell's value is cell's value is automatically automatically recalculated recalculatedNeural NetworksNeural NetworksAlso known asAlso known asConnectionist Connectionist systemssystemsAdaptive systemsAdaptive systemsParallel distributed Parallel distributed processing systemsprocessing systemsNeural network computers are composed of a Neural network computers are composed of a large number of simple processing elements that large number of simple processing elements that individually handle one piece of a much larger individually handle one piece of a much larger problemproblemNeural NetworksNeural NetworksA neural net consists A neural net consists of processing of processing elements (PEs), elements (PEs), which multiply inputs which multiply inputs by various sets of by various sets of weights, yielding a weights, yielding a single output valuesingle output value As a neural network As a neural network is learning, it can is learning, it can make a mistake make a mistake When it does, When it does, weights and weights and thresholds must be thresholds must be changed to changed to compensate for the compensate for the errorerrorExample >>Example >>Chicken exampleChicken exampleYou show a child a You show a child a chicken and tell him it's chicken and tell him it's a birda birdThen you show the child Then you show the child an owlan owlHowever, if we show a child a chicken, a However, if we show a child a chicken, a sparrow, a duck, a hawk, a pelican, and a crow, sparrow, a duck, a hawk, a pelican, and a crow, then it's more likely he'll make the conclusion that then it's more likely he'll make the conclusion that it is a bird.it is a bird.It's likely that the child It's likely that the child won't recognize the owl won't recognize the owl as a birdas a birdCommonly used for Commonly used for Quality controlQuality controlFinancial and economic forecastingFinancial and economic forecastingSpeech and pattern recognitionSpeech and pattern recognitionHealth care cost reductionHealth care cost reductionBankruptcy predictionBankruptcy predictionMachine DiagnosticsMachine DiagnosticsSecurities tradingSecurities tradingTarget MarketingTarget MarketingCommonly used for Commonly used for Artificial Intelligence Artificial Intelligence Weather forecastingWeather forecastingOil and gas exploration Oil and gas explorationSystolic ArraysSystolic ArraysThey are a network They are a network of processing of processing elements that elements that rhythmically compute rhythmically compute data by circulating it data by circulating it through the systemthrough the systemA systolic array is a pipe network A systolic array is a pipe network arrangement of processing units called cells arrangement of processing units called cells where each cell compute data and store it where each cell compute data and store it independently of one anotherindependently of one anotherSystolic ArraysSystolic ArraysProsProsConsConsHighly specialized Highly specialized thus inflexible to thus inflexible to problems to solveproblems to solveShort connectionsShort connectionsSimple designSimple designHighly scalableHighly scalableRobustRobustCompactCompactEfficientEfficientCheapCheapCommonly used for Commonly used for repetitive tasks such as:repetitive tasks such as:Fourier transformationsFourier transformationsImage processingImage processingData compressionData compressionShortest path problemsShortest path problemsSortingSortingSignal processingSignal processingMatrix computationsMatrix
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