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Mulccore in Space Jet Propulsion Laboratory California Ins5tute of Technology Mul$core in Space NASA and AFRL Invest in the Future of Flight Compu;ng Richard Doyle and Raphael Some Jet Propulsion Laboratory, California Instute of Technology Gabriel Mounce, Air Force Research Laboratory, Kirtland AFB Wesley Powell, NASA Goddard Space Flight Center Montgomery Goforth, NASA Johnson Space Center Stephen Horan, NASA Langley Research Center Michael Lowry, NASA Ames Research Center presented at Future In-Space Opera$ons Working Group September 17, 2014 © 2014 California Ins5tute of Technology. Government sponsorship acknowledged. The NASA Need There are important mission scenarios that today cannot be accomplished robustly and cost-effecvely • Space-based compu5ng has not kept up with the needs of current and future NASA missions • Government and industry are developing high- performance space-qualifiable processors • NASA con5nues to have unique requirements – Deep space, long duraon, robo5c and human missions – Higher performance, smaller spacecra and lower cost • Onboard science data processing • Autonomous operaons and greater resilience – Extreme needs for low power and energy management, efficiency, fault tolerance and resilience 9/16/14 2 NASA Space Technology Mission Directorate The Game Changing Development Program (GCDP) commissioned a High Performance Spaceflight Compu6ng (HPSC) Study Iden5fy Driving Capture Applicaons Desirements (Human) (HEOMD) Derive Requirements Perform Trade: Iden5fy Driving Capture Applicaons Desirements Architectures vs. (Robo5c) (SMD) KPPs Perform Gap Analysis Derive Results Review and Evaluate each Analyze Architecture for Compung each Key Make Architectures Performance Recommendaons Parameter (KPP) 9/16/14 3 HPSC Formulaon Phase Study Summary General-purpose mul6-core provides op6mum ROI for NASA The Assignment The Results Iden$fy relevant NASA use cases Developed 9 human spaceflight (HEOMD) and 10 science mission (SMD) What are the paradigm-shiOing NASA space-based use cases for future flight compu5ng, spanning cri5cal mission applicaons that will drive next generaon flight func5ons, high data rate instruments, and autonomy u5lizing model- compung? based reasoning techniques Derive requirements 100X performance increase, low power (down to 7W) with scaling, What are the future onboard compu5ng support for a range of fault tolerance, common programming requirements? languages, avoidance of addi5onal V&V effort, interoperable with co- processors Perform a gap analysis No exis5ng or emerging spaceflight processors possess all necessary How/where do commercial and defense industry performance, power efficiency, reliability, and programmability developments in compung fall short of NASA’s unique aributes requirements and architectural needs? Trade architectures against defined Key Performance Rad-hard general-purpose mul5-core best addresses the future flight Parameters (KPPs) compu5ng requirements and presents the most affordable gap against Which compu5ng architecture will make the most the KPPs difference? Make a recommenda$on Competed/directed program plan for rad-hard general-purpose mul5- How can NASA best invest limited resources to meet core, with solu5ons for power/energy, fault tolerance and other NASA the future needs of its space systems? requirements, leveraging other agency and industry investments 9/16/14 Specifics appear in the following charts 4 NASA Applicaons for High Performance Spaceflight Compu6ng Human Spaceflight Science Mission (HEOMD) Use Cases (SMD) Use Cases 1. Cloud Services 1. Extreme Terrain Landing 2. Advanced Vehicle Health 2. Proximity Operaons / Formaon Management Flying 3. Crew Knowledge Augmentaon 3. Fast Traverse Systems 4. New Surface Mobility Methods 4. Improved Displays and Controls 5. Imaging Spectrometers 5. Augmented Reality for 6. Radar Recogni5on and Cataloging 7. Low Latency Products for Disaster 6. Tele-Presence Response 7. Autonomous & Tele-Robo5c 8. Space Weather Construc5on 9. Science Event Detec5on and 8. Automated Guidance, Navigaon, Response and Control (GNC) 10. Immersive Environments for 9. Human Movement Assist Science Ops / Outreach High value and mission crical applicaons idenfied by 9/16/14 NASA scien$sts and engineers 5 Science Mission Applicaons 10X improvement for exis6ng applica6ons Enables new science and mission capabili6es on future missions SMD Use Cases Benefits to Missions 1. Extreme Terrain Landing • Extreme Terrain Landing 2. Proximity Operaons / – Enables reliable and safe landing in Formaon Flying hazardous terrain: TRN and HDA 3. Fast Traverse algorithms benchmarked by Mars 4. New Surface Mobility Methods Program - required six (6) dedicated RAD750s 5. Imaging Spectrometers 6. Radar • Fast Traverse 7. Low Latency Products for – Remove computaon as a limi5ng factor Disaster Response to mobility – drive 10X faster and more, safely (wheel slip, obstacle detec5on) 8. Space Weather 9. Science Event Detec$on and • Science Event Detec$on and Response Response 10. Immersive Environments for – Increase capture rate for dynamic, Science Ops / Outreach transient events from ~10% to >75%, with <5% false posi5ves, for increased and more 5mely science return No longer need to size science / mission scope 9/16/14 to flight compung capability 6 Deriving Requirements Requirements Template • Space Environment(s) – Radiaon environment at the 5me of applicaon; e.g., geosynchronous (GEO), low-Earth orbit (LEO), deep space? • Spacecra Power Environment(s) / Constraint(s) – Available power for avionics and compu5ng, e.g., small spacecra or rover with limited power availability (6 Was processor power, 10-15 Was computer power), medium sized spacecra (7-12 Was processor power, 15-30 Was computer power), or large spacecra with large power budget (>12-25 Was processor power, >30-100 Was computer power)? • Cri5cality/Fault Tolerance – Is this applicaon life or mission cri5cal, must it operate through faults, can it tolerate errors if detected? • Real-Time – Does the applicaon have a hard real-5me deadline; if so, what is the required 5ming? • Type(s) of Processing – Algorithm kernel(s). Is it primarily e.g., digital signal processing (DSP), data base query, is it parallelizable, is it amenable to a data flow approach? • Memory Access Paern – What is the primary data movement paern, e.g., does it fetch data from memory once and then flow through a processing chain, or does it access data in a con5nuous random access paern, or does it access sequen5al data in a con5nuous and regular paern? • Duty Cycle – What is the paern of execu5on, e.g., is the applicaon called con5nuously over a long period of 5me, is it called once and operate for only a short duraon, is the applicaon execu5on profile spiky and/or unpredictable? • Data Rate 9/16/14 – What are the I/O and memory access data rates? 7 NASA Flight Compu5ng High-Level Requirements as derived from the NASA use cases Computaon Mission Need Objecve of Flight Architecture Processor Type and Category Computaon Aribute Requirements Vision-based • Terrain Relave • Conduct safe proximity • Severe fault tolerance • Hard real me / mission crical Algorithms Navigaon (TRN) operaons around and real-5me • Connuous digital signal processing with Real-Time • Hazard Avoidance primive bodies requirements (DSP) + sequen$al control processing Requirements • Entry, Descent & • Land safely and • Fail-operaonal (fault protec$on) Landing (EDL) accurately • High peak power • High I/O rate • Pinpoint Landing • Achieve robust results needs • Irregular memory use within available • General-purpose (GP) processor 5meframe as input to (10’s – 100’s GFLOPS) + high I/O rate, control decisions augmented by co-processor(s) Model-Based • Mission planning, • Con5ngency planning to • High computaonal • So real me / crical Reasoning scheduling & mi5gate execu5on complexity • Heurisc search, data base Techniques for resource failures • Graceful degradaon operaons, Bayesian inference Autonomy management • Detect, diagnose and • Memory usage (data • Extreme intensive & irregular • Fault management recover from faults movement) impacts memory use (mul-GB/s) in uncertain energy management • > 1GOPS GP processor arrays with environments low latency interconnect High Rate High resolu5on • Downlink images and • Distributed, dedicated • So real me Instrument sensors, e.g., SAR, products rather than raw processors at sensors • DSP/Vector processing with Data Hyper-spectral data • Less stringent fault 10-100’s GOPS (high data flow) Processing • Opportunis5c science tolerance • GP array (10-100’s GFLOPS) required for feature ID / triage 9/16/14 Future NASA use cases require drama$c improvement over RAD750 (~400 MOPS) 8 Mapping of Use Cases to the Computa6on Categories Crew Knowledge Extreme Terrain Landing Improved Displays & Augmentaon Systems Controls Automated Guidance, Immersive Environments Vision Based Algorithms with Augmented reality for Nav & Controls (GNC) for Science Ops Outreach Real Time Requirements Recogni5on & Cataloging Human Movement Assist Fast Transverse Autonomous Mission Planning Advanced Vehicle Health Management Proximity Operaons / Model Based Reasoning Autonomous & Tele- Formaon Flying Techniques Robo5c Construc5on New Surface Mobility Methods Imaging Spectrometers Tele-Presence Low Latency Products for High Rate Instrument Data Radar Disaster Response Processing Cloud Services Space Weather 9 HEOMD SMD Compu5ng Architectures Candidates evaluated under the HPSC task • General-purpose mul-core – Rad-hardened – COTS • DSP mul-core – Rad-hardened – COTS • Reconfigurable
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