Experimental Research

Experimental Research Alberto Sangiovanni-Vincentelli UC Berkeley

Overview • Experimental research is an essential component of CHESS • Feedback on approach • Inspiration for new theory • Impact • Wide range • Industrial and Government test cases • Automotive (safety-critical distributed systems) to be covered in the afternoon • System-on-Chip (high-complexity platforms) • Signal Processing Applications • Hierarchical and Distributed Control • Internal experimental test benches • Wireless Sensor Networks (security, low power) • UAVs (complex control, sensor integration) • New domains: • Hybrid Systems in Systems Biology "Experimental Research", ASV

Overarching Criteria • An application should exercise • Theory: hybrid models, Models of Computation, control algorithms • Tools and Environments • Path to implementation • An application should be relevant for industry or for government agencies "Experimental Research", ASV

Some Applications Addressed Automotive Avionics: UAVs Systems Biology Networked Embedded Systems "Experimental Research", ASV

Outline • Industrial cases • System-on-Chip (high-complexity platforms) • Signal Processing Applications • Hierarchical and Distributed Control • Internal experimental test benches • UAVs (complex control, sensor integration) • New domains: • Hybrid Systems in Systems Biology "Experimental Research", ASV

Metropolis and Xilinx Characterization Environment Real Performance Data ML310 Abstract Modular Model Narrow the Gap Synthesis File Xilinx Virtex II Metropolis currently has a flow to automatically generate sample architectures, extract performance information, and use that information dynamically during simulation. D. Densmore, A.Donlin, A. Sangiovanni-Vincentelli, “FPGA Architecture Characterization for System Level Performance Analysis”, Design Automation and Test Europe (DATE), 2006. (to appear) "Experimental Research", ASV

Metropolis Xilinx Design Environment Real Performance Data ML310 Abstract Modular Model Narrow the Gap Synthesis File Xilinx Virtex II Metropolis currently has a library of Xilinx based components which a designer can instantiate as an architecture instance. When composed their structure can be extracted for performance data or structural synthesis flows. "Experimental Research", ASV

Xilinx Example Designs • Metropolis and Xilinx flow highlights: • Produces accurate simulation results with fidelity. • Can capture structural effects like clock cycle and resource usage. • Large portions automatic, independent, and one time cost operations. "Experimental Research", ASV

Scan Color Conv. ZigZag Mult RLE Lookup 1D-DCT Trans- pose 1D-DCT Trans- pose Add2 Mult1 Add4 Sub2 Merge Shift -128 Mult2 Sub4 Intel JPEG Encoder Application Pre- processing DCT Quantization Huffman "Experimental Research", ASV

Intel MXP5800 Architecture • Designed for Imaging Applications • Highly Heterogeneous Programmable Platform • Top Level: 8 Image Signal Processors with Mesh "Experimental Research", ASV

Design Space Exploration • Replication of scenarios from Intel library • Accurate Performance Modeling • Easy implementation of additional scenarios [A. Davare, Q. Zhu, J. Moondanos, ASV, “JPEG Encoding on the MXP5800: A Platform-based Design Case Study,” Proceedings of EstiMedia 2005] "Experimental Research", ASV

FPAA FPGA Predominantly analog processing Picoradio Baseband System-Level Design Explored the different partitioning between analog and digital Early-Late Gate synchronization algorithm (timing recovery) "Experimental Research", ASV

Design Space Exploration for Integrator • Define configuration space • different biasing, different device sizings, etc. • Impose constraints • bounding ranges for devices size, biasing conditions, etc. • Characterization framework • Matlab client: generates configurations, and the configuration space is statistically sampled • Ocean server: manages circuit simulation in Spectre and extracts performance figures • Generate feasible performance space Explore the Analog Platforms "Experimental Research", ASV

Signal Processing Platform (SPP) Toolchain:Supported Activities (1) System Modeling Goal: Component-based development of large-scale, hard real-time embedded signal processing systems Component Modeling Analysis/Simulation Translation Functional Validation Latency Design Space Modeling Component Core Modeling Timing Generative Modeling Analysis Interchange Format (xAIF) Metamodel Verification Data-Type Dependency Platform Integration Modeling Model Components Dataflow Dependency Platform Modeling Configuration Translation CoActive Platform Configuration Generate Configuration Used by: Raytheon, for embedded DSP applications Available via: ESCHER Platform Wrapper Synthesis HW/SW Partitioning Build Component Allocation Test Structural Optimization Instrumentation SPML/GME SPML/GME Translators Builder,Translator Modeling Environment "Experimental Research", ASV

Signal Processing Platform (SPP) Toolchain:Tool Components (2) SPML/GME System Design Space Optimization Tools DESERT Design space exploration S2D D2S Analysis Tools MATLAB Functional Validation Signal Flow Modeling SPML/GME Point-Design Configuration Simulink/ Stateflow S2A AIRES Schedulability Ptolemy S2C CO-Active Execution Platform Libraries VHDL CONF Comm Interf Target HW "Experimental Research", ASV

RELEX Fault Trees AiTR Service Workflow Model w/Fault Info MCS SW Software Component Model ARV-A(L) SW Services Model MFD Typical Latency FD Network Connectivity Model WC Latency DM PCM FM 10 15 10 15 10 15 10 15 IO Time (in ms) 100 0 Large-scale, real-time embedded system architecture modeling and analysis Safety Models C4ISR-FP FT Safety Models DES Models Performance Analysis C4ISR SIM Goal: Architectural modeling and analysis of very large-scale, distributed real-time embedded systems. Used by: Boeing and SAIC, for analysis of embedded systems architectures. Anticipated code size: 30M SLOC Scenario Net Conditions ARINC 653 “Partitioned” Processor Utilization Network Utilization "Experimental Research", ASV

Model-based Tools for Embedded Fault Diagnostics and Reconfigurable Control • Visual modeling tool for creating: • Physical models of the “plant” • Controller models (incl. reconfiguration) Controller Models Strategy Models Hybrid Diagnostics Active • Modular run-time environment contains: • Hybrid observer and fault detectors • Hybrid and Discrete diagnostics modules • Controller model library • Reconfigurable controller Model Failure Propagation Controller Diagnostics Selector Plant Models Fault Detector Hybrid Observer Interface & Controllers Reconfiguration Manager Run-time Platform (RTOS) Used by: Boeing for autonomous vehicles. "Experimental Research", ASV

Outline • Industrial cases • System-on-Chip (high-complexity platforms) • Signal Processing Applications • Hierarchical Distributed Control • Internal experimental test benches • UAVs (complex control, sensor integration) • New domains: • Hybrid Systems in Systems Biology "Experimental Research", ASV

Hierarchical Distributed Control • Model-based approach using Limited Lookahead Methods • Application: Complex systems made up of interacting subsystems; Challenge: Hierarchical control of Advanced Life Support (ALS) Systems for NASA – regenerative systems • Problem Specification: • Dynamic model of subsystems expressed as hybrid discrete-time equations • Controller input discretized to finite number of values, i.e., control input – finite space • There exist buffers (real or virtual) between subsystems • Individual independent controllers for subsystems, interactions handled through higher level controllers • Modeling abstractions that focus on buffer input/output relations provide the framework for building models at higher levels. • Design model-based controllers with limited look-ahead schemes that search for optimal control input in finite space. "Experimental Research", ASV

Set point control Distributed Control applied to Advanced Life Support (ALS) Systems Constraint-based Distribution of resources Weekly crew schedule Global Controller Supervisory Controller Utility-based Optimize performance AES Controller WRS Controller Crew Scheduler Controller Controller Power Generation WRS System AES System Crew Chamber WRS System ARS System BWP RO AES CDRA SABATIER OGS LC-SAB LC-CDRA LC-OGS LC-AES LC-RO LC-BWP "Experimental Research", ASV

ALS: Data flow + Control Measurement Command MassFlow water_level System Resources Monitor Supervisory Controller O2_level power_level ARS_mode WRS_mode week_schedule H2T_L_ARS ARS Controller O2T_L_ARS Estimation module CO2T_L_ARS CDRM_mode, CDRM_time OGS_mode, OGS_time Crew Controller eCW_FI_CRW eWW_FO_CRW eCW_FO_ARS eWW_FI_ARS HCA_FO_CRW CRW_state CO2_FI_ARS day_schedule WRS Controller CDRA WW_L_WRS CW_L_WRS CCH_state LCA_FO_ARS Crew CO2 RO_mode, RO_time Crew Chamber AES_mode, AES_time CO2_FO_ARS SABATIER O2 Reg. PA_FI_CRW AES RO H2_FI_ARS O2T H2T CW_FI_CRW WWT CWT PPS BWP CW_FO_WRS WW_FI_WRS O2_FO_ARS OGS CW_FI_ARS H2_FO_ARS WW_FO_CRW WW_FO_AES "Experimental Research", ASV

Evaluate controller performance for 90 day challenge mission – 4 astronauts in lunar habitat Results Potable water: Initial: 650 liters; End: 200 liters Energy stored: Min: 200 kW-hour; Max: 1300 kW-hour CO2 tank: Initial = 0 kg; Max = 2.6 kg; Min = 1.4 kg Oxygen tank: Initial = 9.9 kg; Max = 10 kg; Min = 9.9 kg "Experimental Research", ASV

Stability Analysis for Limited Lookahead Control • System Dynamics • Single-Mode Discrete-Time • One-step online control policy B(r,xs) Q set of all states from which a control action is available to move the system closer to xs xs • Technical Results To find B(xs) find (NLP) where Theorem: B(r,xs) is the minimal containable region of xs To determine finite reachability Theorem: B(r,xs)  Q  B(r,xs) is finitely reachable from xRn • Objective • For a domain D and an initial state • xsD, decide if there is a neighbor- • hood B(r,xs)  D of xs such that: • B(r,xs) is finitely reachable from any point in D • The system remains in B(xs) under the online control law "Experimental Research", ASV

Time-Triggered Software for UAV • Real-time systems, e.g., automobile control system, flight controlsystem, air traffic control system etc, must produce their resultswithin specified time intervals. • Real-time systems can beclassified to event-triggered systems and time-triggered systems. • In the event-triggered system, all tasks areinitiated by an event which can be sensor inputs or results ofother tasks etc. It may be hard tospecify precise time for any actiondue to variance of time of an event, which results in jittering ofthe system. • In the time-triggered system, all tasks areinitiated by predetermined points in time. • A missed instant of any action can result in acatastrophe, possibly including the loss of human life, in hardreal-time system. • A hard real-time application demands apredictable, reliable and timely operation which a time-triggeredsystem is able to guarantee. "Experimental Research", ASV

Time-triggered Embedded Control S/W INITIALIZE 200ms GPS ESTIMATE buffer 10ms 20ms Actuator HOVER INS 10ms CRUISE Plant : Berkeley Autonomous Helicopter • Radio controlled helicopter from YAMAHA • Control software was originally designed based on an event-triggered architecture • We have decided to design and implement time-triggered embedded control software for the UAV as above "Experimental Research", ASV

Mode switch Hover mode Cruise mode waypoint waypoint waypoint waypoint ins ins ins ins gps gps gps gps position task1 task2 task1 task2 position position position servos servos 10ms 10ms 10ms 10ms Time-triggered executing sequence • Reading sensor inputs, writing actuator outputs and changing mode are happening at points of predetermined real time • The time-triggered embedded architecture provides predictable (deterministic) operations of software "Experimental Research", ASV

Test Results: Hovering and Cruising "Experimental Research", ASV

Summary • Time-triggered embedded control software was designed and implemented for the Berkeley autonomous helicopter system • Embeddedcontrol software was implemented with modularity in mind to keepthe software clean and make it easy to read and enhance • Software is structured to have multi-mode and mode switches among modes. New modes can be added and the current mode can be modified or removed with relative ease • Designed software was mounted and tested on the safety critical helicoptersystem "Experimental Research", ASV

Outline • Industrial cases • System-on-Chip (high-complexity platforms) • Signal Processing Applications • Hierarchical Distributed Control • Internal experimental test benches • UAVs (complex control, sensor integration) • New domains: • Hybrid Systems in Systems Biology "Experimental Research", ASV

mature subtilin SpaK p SpaB SpaT signal transduction modification transport cleavage subtilin precursor SpaR~p SpaC spaB spaC spaF spaE spaG spaR spaK spaI spaT spaS SpaF immunity SpaE+SpaG Antibiotic biosynthesis in Bacillus subtilis SpaI SigH = discrete states (with randomness) input modeling with hybrid system = continuous states SigH output SpaS SpaRK spaS spaRK S2 S1 "Experimental Research", ASV

Planar cell polarity in Drosophila • Simulations • Parameters estimation • Study of mutants phenotype cell model proteins feedback network "Experimental Research", ASV

Box Invariance for biological reactions systems A dynamical system is said to be box invariant if there exist a box-shaped invariance set around its equilibrium point(s) • Concept of “Set Invariance” around the system equilibrium/a • Naturally prone to describe biological systems (modeled via rate equations) • More flexible than classical notion of (Lyapunov) stability • Yields itself to describe robustness properties • Closely related to lots of concepts from linear algebra and systems theory • Can specify logical conditions for verification purposes Claim : most of the stable biological reactions systems are indeed “box invariant” Very descriptive concept. In Collaboration with the A. Tiwari, SRI International "Experimental Research", ASV

Quantitative and Probabilistic Extensions of Pathway Logic • Pathway Logic(SRI Int.): • tool for symbolic modeling of biological pathways • based on formal methods and rewriting logic • Protein functional domains • and their interactions • Queries performed through formal methods • Extensions: • reasoning with quantitative data • probabilistic interactions between different domains In Collaboration with the PL team, SRI International "Experimental Research", ASV

Experimental Research