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A tunable level, phase change material-based metasurface near IR optical filter on a transmissive doped silicon-on-insulator platform is demonstrated. This type of filter is non-volatile, non-mechanical, with switching being enabled by Joule heating via current pulses.
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We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.
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The Adaptive Instrument Module (AIM) will be the first true demonstration of reconfigurable computing with field-programmable gate arrays (FPGAs) in space, enabling the 'brain' of the system to evolve or adapt to changing requirements. In partnership with NASA Goddard Space Flight Center and the Australian Cooperative Research Centre for Satellite Systems (CRC-SS), APL has built the flight version to be flown on the Australian university-class satellite FEDSAT. The AIM provides satellites the flexibility to adapt to changing mission requirements by reconfiguring standardized processing hardware rather than incurring the large costs associated with new builds. This ability to reconfigure the processing in response to changing mission needs leads to true evolveable computing, wherein the instrument 'brain' can learn from new science data in order to perform state-of-the-art data processing. The development of the AIM is significant in its enormous potential to reduce total life-cycle costs for future space exploration missions. The advent of RAM-based FPGAs whose configuration can be changed at any time has enabled the development of the AIM for processing tasks that could not be performed in software. The use of the AIM enables reconfiguration of the FPGA circuitry while the spacecraft is in flight, with many accompanying advantages. The AIM demonstrates the practicalities of using reconfigurable computing hardware devices by conducting a series of designed experiments. These include the demonstration of implementing data compression, data filtering, and communication message processing and inter-experiment data computation. The second generation is the Adaptive Processing Template (ADAPT) which is further described in this paper. The next step forward is to make the hardware itself adaptable and the ADAPT pursues this challenge by developing a reconfigurable module that will be capable of functioning efficiently in various applications. ADAPT will take advantage of radiation tolerant RAM-based field programmable gate array (FPGA) technology to develop a reconfigurable processor that combines the flexibility of a general purpose processor running software with the performance of application specific processing hardware for a variety of high performance computing applications.
The motivation for this work comes from an observation that amidst the push for Massively Parallel (MP) solutions to high-end computing problems such as numerical physical simulations, large amounts of legacy code exist that are highly optimized for vector supercomputers. Because re-hosting legacy code often requires a complete re-write of the original code, which can be a very long and expensive effort, this work examines the potential to exploit reconfigurable computing machines in place of a vector supercomputer to implement an essentially unmodified legacy source code. Custom and reconfigurable computing resources could be used to emulate an original application's target platform to the extent required to achieve high performance. To arrive at an architecture that delivers the desired performance subject to limited resources involves solving a multi-variable optimization problem with constraints. Prior research in the area of reconfigurable computing has demonstrated that designing an optimum hardware implementation of a given application under hardware resource constraints is an NP-complete problem. The premise of the approach is that the general issue of applying reconfigurable computing resources to the implementation of an application, maximizing the performance of the computation subject to physical resource constraints, can be made a tractable problem by assuming a computational paradigm, such as vector processing. This research contributes a formulation of the problem and a methodology to design a reconfigurable vector processing implementation of a given application that satisfies a performance metric. A generic, parametric, architectural framework for vector processing implemented in reconfigurable logic is developed as a target for a scheduling/mapping algorithm that maps an input computation to a given instance of the architecture. This algorithm is integrated with an optimization framework to arrive at a specification of the architecture parameters that attempts to minimize execution time, while staying within resource constraints. The flexibility of using a custom reconfigurable implementation is exploited in a unique manner to leverage the lessons learned in vector supercomputer development. The vector processing framework is tailored to the application, with variable parameters that are fixed in traditional vector processing. Benchmark data that demonstrates the functionality and utility of the approach is presented. The benchmark data includes an identified bottleneck in a real case study example vector code, the NASA Langley Terminal Area Simulation System (TASS) application.
Flexible and extensible space communications architectures and technology are essential to enable future space exploration and science activities. NASA has championed the development of the Space Telecommunications Radio System (STRS) software defined radio (SDR) standard and the application of SDR technology to reduce the costs and risks of using SDRs for space missions, and has developed an on-orbit testbed to validate these capabilities. The Space Communications and Navigation (SCaN) Testbed (previously known as the Communications, Navigation, and Networking reConfigurable Testbed (CoNNeCT)) is advancing SDR, on-board networking, and navigation technologies by conducting space experiments aboard the International Space Station. During its first year(s) on-orbit, the SCaN Testbed has achieved considerable accomplishments to better understand SDRs and their applications. The SDR platforms and software waveforms on each SDR have over 1500 hours of operation and are performing as designed. The Ka-band SDR on the SCaN Testbed is NASAs first space Ka-band transceiver and is NASA's first Ka-band mission using the Space Network. This has provided exciting opportunities to operate at Ka-band and assist with on-orbit tests of NASA newest Tracking and Data Relay Satellites (TDRS). During its first year, SCaN Testbed completed its first on-orbit SDR reconfigurations. SDR reconfigurations occur when implementing new waveforms on an SDR. SDR reconfigurations allow a radio to change minor parameters, such as data rate, or complete functionality. New waveforms which provide new capability and are reusable across different missions provide long term value for reconfigurable platforms such as SDRs. The STRS Standard provides guidelines for new waveform development by third parties. Waveform development by organizations other than the platform provider offers NASA the ability to develop waveforms itself and reduce its dependence and costs on the platform developer. Each of these new waveforms requires a waveform build environment for the particular SDR, helps assess the usefulness of the platform provider documentation, and exercises the objectives of STRS Standard and the SCaN Testbed. There is considerable interest in conducting experiments using the SCaN Testbed from NASA, academia, commercial companies, and other space agencies. There are approximately 25 experiments or activities supported by the project underway or in development, with more proposals ready, as time and funding allow, and new experiment solicitations available. NASA continues development of new waveforms and applications in communications, networking, and navigation, the first university experimenters are beginning waveform development, which will support the next generation of communications engineers, and international interest is beginning with space agency partners from European Space Agency (ESA) and the Centre National d'Etudes Spatiales (CNES). This paper will provide an overview of the SCaN Testbed and discuss its recent accomplishments and experiment activities.Its recent successes in Ka-band operations, reception of the newest GPS signals, SDR reconfigurations, and STRS demonstration in space when combined with the future experiment portfolio have positioned the SCaN Testbed to enable future space communications and navigation capabilities for exploration and science.
A method for evolving a circuit comprising configuring a plurality of transistors using a plurality of reconfigurable switches so that each of the plurality of transistors has a terminal coupled to a terminal of another of the plurality of transistors that is controllable by a single reconfigurable switch. The plurality of reconfigurable switches being controlled in response to a chromosome pattern. The plurality of reconfigurable switches may be controlled using an annealing function. As such, the plurality of reconfigurable switches may be controlled by selecting qualitative values for the plurality of reconfigurable switches in response to the chromosomal pattern, selecting initial quantitative values for the selected qualitative values, and morphing the initial quantitative values. Typically, subsequent quantitative values will be selected more divergent than the initial quantitative values. The morphing process may continue to partially or to completely polarize the quantitative values.
Sectors may combine or split within areas of specialization in response to changing traffic patterns. This method of managing capacity and controller workload could be made more flexible by dynamically modifying sector boundaries. Much work has been done on methods for dynamically creating new sector boundaries [1-5]. Many assessments of dynamic configuration methods assume the current day baseline configuration remains fixed [6-7]. A challenging question is how to select a dynamic configuration baseline to assess potential benefits of proposed dynamic configuration concepts. Bloem used operational sector reconfigurations as a baseline [8]. The main difficulty is that operational reconfiguration data is noisy. Reconfigurations often occur frequently to accommodate staff training or breaks, or to complete a more complicated reconfiguration through a rapid sequence of simpler reconfigurations. Gupta quantified a few aspects of airspace boundary changes from this data [9]. Most of these metrics are unique to sector combining operations and not applicable to more flexible dynamic configuration concepts. To better understand what sort of reconfigurations are acceptable or beneficial, more configuration change metrics should be developed and their distribution in current practice should be computed. This paper proposes a method to select a simple sequence of configurations among operational configurations to serve as a dynamic configuration baseline for future dynamic configuration concept assessments. New configuration change metrics are applied to the operational data to establish current day thresholds for these metrics. These thresholds are then corroborated, refined, or dismissed based on airspace practitioner feedback. The dynamic configuration baseline selection method uses a k-means clustering algorithm to select the sequence of configurations and trigger times from a given day of operational sector combination data. The clustering algorithm selects a simplified schedule containing k configurations based on stability score of the sector combinations among the raw operational configurations. In addition, the number of the selected configurations is determined based on balance between accuracy and assessment complexity.
An error bound is derived for a reliability model approximation method. The approximation method is appropriate for the semi-Markov models of reconfigurable systems that are designed to achieve extremely high reliability. The semi-Markov models of these system are complex, and a significant amount of their complexity arises from the detailed descriptions of the reconfiguration processes. The reliability model approximation method consists of replacing a detailed description of a reconfiguration process with the probabilities of the possible outcomes of the reconfiguration process. These probabilities are included in the model as instantaneous jumps from the fault-occurrence state. Since little time is spent in the reconfiguration states, instantaneous jumps are a close approximation to the original model. This approximation procedure is shown to produce an overestimation for the probability of system failure, and an error bound is derived for this overestimation.
The Radiation Hardened Electronics for Space Environments (RHESE) project endeavors to advance the current state-of-the-art in high-performance, radiation-hardened electronics and processors, ensuring successful performance of space systems required to operate within extreme radiation and temperature environments. Because RHESE is a project within the Exploration Technology Development Program (ETDP), RHESE's primary customers will be the human and robotic missions being developed by NASA's Exploration Systems Mission Directorate (ESMD) in partial fulfillment of the Vision for Space Exploration. Benefits are also anticipated for NASA's science missions to planetary and deep-space destinations. As a technology development effort, RHESE provides a broad-scoped, full spectrum of approaches to environmentally harden space electronics, including new materials, advanced design processes, reconfigurable hardware techniques, and software modeling of the radiation environment. The RHESE sub-project tasks are: SelfReconfigurable Electronics for Extreme Environments, Radiation Effects Predictive Modeling, Radiation Hardened Memory, Single Event Effects (SEE) Immune Reconfigurable Field Programmable Gate Array (FPGA) (SIRF), Radiation Hardening by Software, Radiation Hardened High Performance Processors (HPP), Reconfigurable Computing, Low Temperature Tolerant MEMS by Design, and Silicon-Germanium (SiGe) Integrated Electronics for Extreme Environments. These nine sub-project tasks are managed by technical leads as located across five different NASA field centers, including Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, Langley Research Center, and Marshall Space Flight Center. The overall RHESE integrated project management responsibility resides with NASA's Marshall Space Flight Center (MSFC). Initial technology development emphasis within RHESE focuses on the hardening of Field Programmable Gate Arrays (FPGA)s and Field Programmable Analog Arrays (FPAA)s for use in reconfigurable architectures. As these component/chip level technologies mature, the RHESE project emphasis shifts to focus on efforts encompassing total processor hardening techniques and board-level electronic reconfiguration techniques featuring spare and interface modularity. This phased approach to distributing emphasis between technology developments provides hardened FPGA/FPAAs for early mission infusion, then migrates to hardened, board-level, high speed processors with associated memory elements and high density storage for the longer duration missions encountered for Lunar Outpost and Mars Exploration occurring later in the Constellation schedule.
For sustainable Exploration Missions the need exists to assemble systems-of-systems in space, on the Moon or on other planetary surfaces. To fulfill this need new and innovative system architectures must be developed to be modularized and launched with the present lift capability of existing rocket technology. To enable long duration missions with minimal redundancy and mass, system software and hardware must be reconfigurable. This will enable increased functionality and multiple use of launched assets while providing the capability to quickly overcome components failures. Additional required capability includes the ability to dynamically demate and reassemble individual system elements during a mission in order to recover from failed hardware or to adapt to changes in mission requirements. To meet the Space Exploration goals of Interoperability and Reconfigurability, many challenges must be addressed to transform the traditional static avionics architectures into architectures with dynamic capabilities. The objective of this paper is to introduce concepts associated with reconfigurable computer systems; to review the various needs and challenges associated with reconfigurable avionics space systems; to provide an operational example that illustrates the application to both the Crew Exploration Vehicle and a collection of 'Habot-like' mobile surface elements; to summarize the approaches that address key challenges to the acceptance of a Flexible, Intelligent, Modular, Affordable and Reconfigurable avionics space system.
This paper compares airspace design solutions for dynamically reconfiguring airspace in response to nominal daily traffic volume fluctuation. Airspace designs from seven algorithmic methods and a representation of current day operations in Kansas City Center were simulated with two times today's demand traffic. A three-configuration scenario was used to represent current day operations. Algorithms used projected unimpeded flight tracks to design initial 24-hour plans to switch between three configurations at predetermined reconfiguration times. At each reconfiguration time, algorithms used updated projected flight tracks to update the subsequent planned configurations. Compared to the baseline, most airspace design methods reduced delay and increased reconfiguration complexity, with similar traffic pattern complexity results. Design updates enabled several methods to as much as half the delay from their original designs. Freeform design methods reduced delay and increased reconfiguration complexity the most.
For sustainable Exploration Missions the need exists to assemble systems-of-systems in space, on the Moon or on other planetary surfaces. To fulfill this need new and innovative system architecture is needed that can be satisfied with the present lift capability of existing rocket technology without the added cost of developing a new heavy lift vehicle. To enable ultra-long life missions with minimum redundancy and lighter mass the need exists to develop system soft,i,are and hardware reconfigurability, which enables increasing functionality and multiple use of launched assets while at the same time overcoming any components failures. Also the need exists to develop the ability to dynamically demate and reassemble individual system elements during a mission in order to work around failed hardware or changed mission requirements. Therefore to meet the goals of Space Exploration Missions in hiteroperability and Reconfigurability, many challenges must be addressed to transform the traditional static avionics architecture into architecture with dynamic capabilities. The objective of this paper is to introduce concepts associated with reconfigurable computer systems; review the various needs and challenges associated with reconfigurable avionics space systems; provide an operational example that illustrates the needs applicable to either the Crew Exploration Vehicle or a collection of "Habot like" mobile surface elements; summarize the approaches that address key challenges to acceptance of a Flexible, Intelligent, Modular and Affordable reconfigurable avionics space system.