Polymorphous Computing Architectures

1. Polymorphous Computing Architectures Run-time Environment And Design Application for Polymorphous Technology Verification & Validation (READAPT V&V) Lockheed Martin Advanced Technology Laboratories 1 Federal Street • A&E Building Camden, New Jersey 08102

2. Multiple Sensors (A,B,C...X) plug & play Multi- Mission In-Mission Re-target -ability …. A B C X Platform transit Tracking Multi-sensor processing Polymorphous ComputingArchitectures Goal: Computing systems (chips, networks, software) that will verifiably morph to changing missions, sensor configurations, hardware failures, and operational constraints during a mission or over the life of the platform Response or Morph Time: Months Days Seconds Mission Aware Computing

3. Verification & Validation Problem • V&V currently represents 40 to 70% of total system costs • Next generation systems will drive this cost up exponentially • Increasing Scale: Multi-mission roles, improved sensors, etc. • Increasing Complexity • Introduction of autonomy, adaptive mission processing, decision aiding capabilities • High percentage of non-deterministic functionality • Emergence of adaptive computing (PCA, etc.) • Current V&V solutions are costly with limited scalability • Autonomy is driving exponential Growth of Flight-Safety-Critical Systems • Mission-critical Applications project similar growth New approaches to V&V are critical to the affordability and adoption of highly adaptive, cognitive systems

4. Recommended Additions Use the existing design models for V&V before implementation and again during Operation executable spec. Model Simulate model-based V&V Model Generated SW Rapid Prototype Operate Leveraging Design Models for V&V Current Approach Requirements Code Test (V&V) Production System

5. High Payoff Technologies • Enhanced model-based techniques for complex, dynamic systems • Formal methods: Domain-specific approaches provide provably correct designs; dramatic (4-10X) reduction in software, integration, and test costs • Hybrid methods: Combine discrete (state, flow-based) and continuous (system dynamics, temporal) models • Stochastic methods: Complexity- and probability-based characterization of non-deterministic system functions and software • Evolutionary methods: Mutation-based testing and constraint generation • Constraint-driven specification, implementation, and run-time enforcement of emergent behaviors • Information integration across multiple domains/aspects Enhanced model-based techniques optimize test coverage with reduced cost; maximizes V&V payoff for next generation systems

6. READAPT V&V Thesis and Goals • Thesis • Run-Time monitoring and correcting is a revolutionary solution for ensuring “safe” and properly executing behavior for PCA and cognitive systems-based adaptable architectures • READAPT V&V goals • Develop efficient and reliable means of capturing both system behaviors and system designs (model behaviors) • Develop ability to monitor complex, adaptable systems at run-time to behaviors captured during the design process • Develop ability to force a complex system into a properly behaving state in response to changing behaviors and behavior violations

7. Overall Approach • Overall Approach • Summarize requirements and approaches for avionics V&V • Investigate additional V&V requirements for PCA enabled avionics applications • Model a PCA architecture for PCA V&V R&D • Model a non-determinate avionics application (algorithm) for PCA V&V experimentation • Research, develop, and model a hierarchical, integrated, reactive-configuration and behavior-monitoring verification approach for PCA enabled applications • Integrate models to evaluate the V&V research for a PCA enabled avionics application

8. Run-Time Monitoring and Correcting PCA System Specification Application System Specification Requirements Specification formalize model model MEDL Script Application Model compile PCA Platform Model configure output CSIM Core MaC Tool simulation trace

9. CSIM MaCS Monitoring script configure event def. Integrator state records interface objects Exhibit Displays Laptop 2 - Flight with Route Planner Display Laptop 1 - CSIM/MaCS Display • Flight Simulator provides context display • CSIM simulates PCA enabled route planner • CSIM controls CDU on Flight Simulator • MaCS monitors route planner execution & controls PCA & algorithm adaptation Route Planning Algorithm Target Set (Endpoint) Planning Horizon Grid Size C(r,r+dr) Cardinality (Direction) Incremental Cost

10. Overall Demo Scenario Continuous Replan During Avoidance to Include Targeting Threat Avoidance & Targeting Replan Pre-planned Mission Execution Threat Avoidance & Replan Threat Avoidance & Replan States PCA Morph State 3: PCA Morph State 2: PCA Morph State 4: PCA Morph State 2: PCA Morph State 1: Events Approaching SAM site Run-time Monitor Detects performance Envelope problem; Revert back to State 2 (Safe) SAM site Detection & route replan Passed SAM site

11. CSIM PCA Flight Control Display PCA Virtual Processor State and Activity System State and Task Flow Mission Assignment Total active processor count display Threat Avoidance Stream Processors indicated byfilled boxes Comms GP Processors indicated by (non-Red) outlined boxes Flight Control Imaging Threaded Processors indicated by REDoutlined boxes Route Planning Dynamic bar chart indicating total active processors, active stream processors active GP processors and active threaded processors Self Test and MaCS MaCS messages and status Mission status RADAR Tasks