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At least 289 records · Page 16

All sources NASA NTRS DOE OSTI

NASA NTRS1997

Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications

A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.

Fang, Wai-Chi↗

NASA NTRSDate not supplied

Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy- intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware codesign in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing, and describe improved methods for simulating quantum systems of various types on high performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, computational purposes.

quantum computing↗

DOE OSTI2023

Scaling comb-driven resonator-based DWDM silicon photonic links to multi-Tb/s in the multi-FSR regime

The use of chip-based micro-resonator Kerr frequency combs in conjunction with dense wavelength-division multiplexing (DWDM) enables massively parallel intensity-modulated direct-detection data transmission with low energy consumption. Resonator-based modulators and filters used in such systems can limit the number of usable wavelength channels due to practical constraints on the maximum achievable free spectral range (FSR). In this work, we introduce the design of multi-Tb/s comb-driven resonator-based silicon photonic links by leveraging the multi-FSR regime. We demonstrate the viability of the link architecture with yield estimates that are supported by extensive wafer-scale measurements of 704 micro-resonators fabricated in a commercial complementary metal–oxide–semiconductor foundry. We show that a 2.80 Tb/s link is realizable with a ≥6 σ yield (∼99.999%), and that aggregate bandwidths of 3.76 Tb/s and 4.72 Tb/s are possible if yield targets are relaxed (3 σ and 1 σ , respectively). All designs represent a 1.94−3.28× boost to aggregate link bandwidth while maintaining BER≤10 −10 performance, with a theoretical bandwidth of 10.51 Tb/s being possible for sufficiently robust resonators. We use high-speed BER measurements to inform co-optimization of data rate and aggressor spacing ( λ ag ), limiting any additional loss-based power penalties to off-resonance insertion loss (IL) and routing loss. This work demonstrates that, through the multi-FSR regime, there is a clear path toward Kerr comb-driven ultra-broadband, high bandwidth silicon photonic links that can support next-generation data centers and high-performance computers.

James, Aneek (ORCID:0000000262527807)↗

DOE OSTI2023

The ETROC2 prototype for CMS MTD Endcap Timing Layer (ETL) upgrade

The Endcap Timing ReadOut Chip (ETROC) is designed to process LGAD signals with time resolution down to about 40-50ps per hit. The ETROC2 is the first full size (16x16) prototype design with the front-end based on and scaled up from the ETROC1 (4x4). The readout designs at pixel and global level and the system interfaces are all new and are compatible with the final chip specifications in terms of functionality. The ETROC2 is intended as a learning chip, as a stepstone to the ETROC3 which is intended as the pre-production design. The ETROC2 design and test results will be presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

The ETROC2 prototype for CMS MTD Endcap Timing Layer (ETL) upgrade

The ETROC2 prototype for CMS MTD Endcap Timing Layer (ETL) upgrade 520__ $$aThe Endcap Timing ReadOut Chip (ETROC) is designed to process LGAD signals with time resolution down to about 40-50ps per hit. The ETROC2 is the first full size (16x16) prototype design with the front-end based on and scaled up from the ETROC1 (4x4). The readout designs at pixel and global level and the system interfaces are all new and are compatible with the final chip specifications in terms of functionality. The ETROC2 is intended as a learning chip, as a stepstone to the ETROC3 which is intended as the pre-production design. The ETROC2 design and test results will be presented.

Gong, Datao↗

DOE OSTI2023

The ETROC2 prototype for CMS MTD Endcap Timing Layer (ETL) upgrade

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2016

Photonic Integrated Circuit (PIC) Device Structures: Background, Fabrication Ecosystem, Relevance to Space Systems Applications, and Discussion of Related Radiation Effects

Electronic integrated circuits are considered one of the most significant technological advances of the 20th century, with demonstrated impact in their ability to incorporate successively higher numbers transistors and construct electronic devices onto a single CMOS chip. Photonic integrated circuits (PICs) exist as the optical analog to integrated circuits; however, in place of transistors, PICs consist of numerous scaled optical components, including such "building-block" structures as waveguides, MMIs, lasers, and optical ring resonators. The ability to construct electronic and photonic components on a single microsystems platform offers transformative potential for the development of technologies in fields including communications, biomedical device development, autonomous navigation, and chemical and atmospheric sensing. Developing on-chip systems that provide new avenues for integration and replacement of bulk optical and electro-optic components also reduces size, weight, power and cost (SWaP-C) limitations, which are important in the selection of instrumentation for specific flight projects. The number of applications currently emerging for complex photonics systems-particularly in data communications-warrants additional investigations when considering reliability for space systems development. This Body of Knowledge document seeks to provide an overview of existing integrated photonics architectures; the current state of design, development, and fabrication ecosystems in the United States and Europe; and potential space applications, with emphasis given to associated radiation effects and reliability.

Body of Knowledge (BOK)↗

DOE OSTI2024

Assessing and advancing the potential of quantum computing: A NASA case study

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware co-design in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing and describe improved methods for simulating quantum systems of various types on high-performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, as well as insights into fundamental quantum physics that can be harnessed for computational purposes.

Rieffel, Eleanor G.↗

DOE OSTI2021

Ultra-high extinction ratio polarization beam splitter with extreme skin-depth waveguide

In this Letter, we present a high extinction ratio and compact on-chip polarization beam splitter (PBS), based on an extreme skin-depth (eskid) waveguide. Subwavelength-scale gratings form an effectively anisotropic metamaterial cladding and introduce a large birefringence. The anisotropic dielectric perturbation of the metamaterial cladding suppresses the TE polarization extinction via exceptional coupling, while the large birefringence efficiently cross-couples the TM mode, thus reducing the coupling length. We demonstrated the eskid-PBS on a silicon-on-insulator platform and achieved an ultra-high extinction ratio PBS ( ≈ <#comment/> 60 d B for TE and ≈ <#comment/> 48 d B for TM) with a compact coupling length ( ≈ <#comment/> 14.5 µ <#comment/> m ). The insertion loss is also negligible ( < <#comment/> 0.6 d B ). The bandwidth is > <#comment/> 80 (30) nm for the TE (TM) extinction ratio > <#comment/> 20 d B . Our ultra-high extinction ratio PBS is crucial in implementing efficient polarization diversity circuits, especially where a high degree of polarization distinguishability is necessary, such as photonic quantum information processing.

Ahmed, Syed Z.↗

DOE OSTI2022

Runtime Systems for Energy Efficiency in Advanced Computing Systems

As heterogeneous systems become increasingly popular for both mobile and high-performance computing, conventional efficiency techniques such as dynamic voltage and frequency scaling (DVFS) fail to account for the tightly coupled and varied nature of systems on a chip (SoCs). In this work, we explore the impact of system unaware DVFS techniques on a mobile SoC under three benchmark suites: Chai, Rodinia, and Antutu. We then analyze performance trends across the suites to identify a set of consistent operating points that optimally balance power and performance across the system. The consistent operating points are then constructed into a dependency graph which can be leveraged to produce a more effective, SoC-wide governor.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1983

Automatic hardware synthesis

The complexity of the circuit that can fit on an integrated circuit (IC) chip has reached the level of a million transistors with the advent of Very-Large-Scale Integration (VLSI). Several automatic synthesis systems have evolved that 'aid' the human designer in managing this complexity. This paper surveys such efforts. The synthesis is viewed as the process of transforming a high-level design specification into a lower level design specification that includes more structural details, leading to the physical design of the IC. The characteristics of ten automatic synthesis systems are summarized.

Shiva, S. G.↗

NASA NTRS1987

A comparison of VLSI architecture of finite field multipliers using dual, normal or standard basis

Three different finite field multipliers are presented: (1) a dual basis multiplier due to Berlekamp; (2) a Massy-Omura normal basis multiplier; and (3) the Scott-Tavares-Peppard standard basis multiplier. These algorithms are chosen because each has its own distinct features which apply most suitably in different areas. Finally, they are implemented on silicon chips with nitride metal oxide semiconductor technology so that the multiplier most desirable for very large scale integration implementations can readily be ascertained.

Hsu, I. S.↗

NASA NTRS2004

Novel Biomedical Device Utilizing Light-Emitting Nanostructures Developed

Sketches and chemical diagrams of state-of-the-art device and novel proposed device are presented. Current device uses a diode laser that emits into a fluorescent fluid only one wavelength and a photodetector diode that detects only one wavelength. Only one type of bacteria can be detected. The proposed device uses a quantum dot array that emits into a fluorescent fluid multiple wavelengths and an NIR 512 spectrometer that scans 0.8- to 1.7-mm wavelengths. Hundreds of different bacteria and viruses can be detected. A novel biomedical device is being developed at the NASA Glenn Research Center in cooperation with the University of Michigan. This device uses nano-structured quantum dots that emit light in the near-infrared (IR) region. The nanostructured quantum dots are used as a source and excite fluorochrome polymers coupled with antibodies that seek out and attach to specific bacteria and viruses. The fluorochrome polymers/antibodies fluoresce at specific wavelengths in the near-IR spectrum, but these wavelengths are offset from the excitation wavelength and can be detected with a tunable spectrometer. The device will be used to detect the presence of viruses and bacteria in simple fluids and eventually in more complex fluids, such as blood. Current state-of-the-art devices are limited to single bacteria or virus detection and a considerable amount of time and effort is required to prepare samples for analysis. Most importantly, the devices are quite large and cumbersome, which prohibits them from being used on the International Space Station and the space shuttles. This novel device uses nanostructured quantum dots which, through molecular beam epitaxy and highly selective annealing processes, can be developed into an illumination source that could potentially generate hundreds of specific wavelengths. As a result, this device will be able to excite hundreds of antibody/fluorochrome polymer combinations, which in turn could be used to detect hundreds of bacteria and viruses in fluids. A novel sample preparation technique that exploits micromembrane filtration and centrifugation methods has been developed for this device. The technique greatly reduces the time required to prepare the sample and the amount of sample needed to perform an accurate and comprehensive analysis. Last, and probably most important, because of the nano-light-emitting source and the novel sample preparation technique, the overall size of the device could be reduced dramatically. This device will serve as a nanoscale lab-on-a-chip for in situ microorganism detection and will enable tests to be performed on a time scale of minutes rather than days. Thus, it is ideally suited for monitoring the environmental conditions onboard the International Space Station and the space shuttles, thereby enhancing the safety of the astronauts. In addition, the device has important commercial applications, such as detecting the presence of bacteria and viruses in water at food- and beverage-processing centers, water treatment plants, and restaurants. Also, this technology has the potential to be used to detect bacteria and viruses in more complex fluids, such as blood--which in all likelihood would revolutionize blood analysis as it is performed today. This project was made possible through the Director's Discretionary Fund and is ongoing. In addition, this project provides funding to Dr. Rachel Goldman of the University of Michigan for the research and development of nanostructured quantum dots.

Scardelletti, Maximilian C.↗

NASA NTRSDate not supplied

A NASA Perspective on Quantum AI, Error Correction, and Beyond

quantum optimization algorithms and sampling↗

DOE OSTI2020

An integrated computational materials engineering framework to analyze the failure behaviors of carbon fiber reinforced polymer composites for lightweight vehicle applications

A bottom-up multi-scale modeling approach is used to develop an Integrated Computational Materials Engineering (ICME) framework for carbon fiber reinforced polymer (CFRP) composites, which has the potential to reduce development to deployment lead time for structural applications in lightweight vehicles. In this work, we develop and integrate computational models comprising of four size scales to fully describe and characterize three types of CFRP composites. In detail, the properties of the interphase region are determined by an analytical gradient model and molecular dynamics analysis at the nano-scale, which is then incorporated into micro-scale unidirectional (UD) representative volume element (RVE) models to characterize the failure strengths and envelopes of UD CFRP composites. Then, the results are leveraged to propose an elasto-plastic-damage constitutive law for UD composites to study the fiber tows of woven composites as well as the chips of sheet molding compound (SMC) composites. Subsequently, the failure mechanisms and failure strengths of woven and SMC composites are predicted by the meso-scale RVE models. Finally, building upon the models and results from lower scales, we show that a homogenized macro-scale model can capture the mechanical performance of a hat-section-shaped part under four-point bending. Along with the model integration, we will also demonstrate that the computational results are in good agreement with experiments conducted at different scales. The present work illustrates the potential and significance of integrated multi-scale computational modeling tools that can virtually evaluate the performance of CFRP composites and provide design guidance for CFRP composites used in structural applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗

DOE OSTI2024

FPGA-Accelerated Range-Limited Molecular Dynamics

Long timescale Molecular Dynamics (MD) simulation of small molecules is crucial in drug design and basic science. To accelerate a small data set that is executed for a large number of iterations, high-efficiency is required. Recent work in this domain has demonstrated that among COTS devices only FPGA-centric clusters can scale beyond a few processors. The problem addressed here is that, as the number of on-chip processors has increased from fewer than 10 into the hundreds, previous intra-chip routing solutions are no longer viable. We find, however, that through various design innovations, high efficiency can be maintained. These include replacing the previous broadcast networks with ring-routing and then augmenting the rings with out-of-order and caching mechanisms. Others are adding a level of hierarchical filtering and memory recycling. Two novel optimized architectures emerge, together with a number of variations. These are validated, analyzed, and evaluated. We find that in the domain of interest speed-ups over GPUs are achieved. Finally, the potential impact is that this system promises to be the basis for scalable long timescale MD with commodity clusters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Mosaics , The Best of Both Worlds: Analog devices with Digital Spiking Communication to build a Hybrid Neural Network Accelerator

Neuromorphic architectures have seen a resurgence of interest in the past decade owing to 100x-1000x efficiency gain over conventional Von Neumann architectures. Digital neuromorphic chips like Intel's Loihi have shown efficiency gains compared to GPUs and CPUs and can be scaled to build larger systems. Analog neuromorphic architectures promise even further savings in energy efficiency, area, and latency than their digital counterparts. Neuromorphic analog and digital technologies provide both low-power and configurable acceleration of challenging artificial intelligence (AI) algorithms. We present a hybrid analog-digital neuromorphic architecture that can amplify the advantages of both high-density analog memory and spike-based digital communication while mitigating each of the other approaches' limitations.

97 MATHEMATICS AND COMPUTING↗