Group items tagged

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications,healthcare informatics and business analytics. A salient feature of these emerging domains is the large and continuously streaming data sets that these applications generate, which must be processed efficiently enough to support real-time learning and decision making based on these data. This challenge requires novel hardware techniques and machine-learning architectures.This solicitation seeks to lay the foundation for next-generation co-design of RTML algorithms and hardware, with the principal focus on developing novel hardware architectures and learning algorithms in which all stages of training (including incremental training, hyperparameter estimation, and deployment) can be performed in real time. The National Science Foundation (NSF) and the Defense Advanced Research Projects Agency (DARPA) are teaming up through this Real-Time Machine Learning (RTML) program to explore high-performance, energy-efficient hardware and machine-learning architectures that can learn from a continuous stream of new data in real time, through opportunities for post-award collaboration between researchers supported by DARPA and NSF.

The purpose of this amendment is to correct a typographical error in the abstract details on page 41. See the attached conformed BAA with changes highlighted in yellow. Amendment 01: The purpose of this amendment is to make administrative changes as highlighted in yellow in the attached.Original Synopsis Below:DARPA is soliciting innovative research proposals in the area of novel computing architectures. The Page 3 Architectures thrust of the Electronics Resurgence Initiative (ERI) seeks to demonstrate heterogeneous computing systems that provide the performance advantages of specialized processors, while maintaining the programmability of general purpose processors.The goal of the Software Defined Hardware (SDH) program is to build runtime-reconfigurable hardware and software that enables near ASIC performance without sacrificing programmability for data-intensive algorithms. SDH will create a hardware/software system that allows data-intensive algorithms to run at near ASIC efficiency without the cost, development time or single application limitations associated with ASIC development. The overall goal of the Domain-specific System on Chip (DSSoC) program is to develop a heterogeneous SoC comprised of many cores that mix general-purpose processors, special-purpose processors, hardware accelerators, memory, and input/output (I/O). DSSoC seeks to enable rapid development of multi-application systems through a single programmable device.

The need to process large data sets arising in many practical problems require real-time learning from data streams makes high-performance hardware necessary, and yet the very nature of these problems, along with currently known algorithms for addressing them, imposes significant hardware challenges. Current versions of deep-learning algorithms operate by using millions of parameters whose optimal values need to be determined for good performance in real time on high-performance hardware. Conversely, the availability of fast hardware implementations can enable fuller use of Bayesian techniques, attractive for their ability to quantify prediction uncertainty and thus give estimates of reliability and prediction breakdown. The abilities of ML systems to self-assess for reliability and predict their own breakdowns (and also recover without significant ill effects) constitute critical areas for algorithm development as autonomous systems become widely deployed in both decision support and embodied AI agents. Only with attention to these challenges can we construct systems that are robust when they encounter novel situations or degradation and failure of sensors.

The NSF/Intel Partnership on Computer Assisted Programming for Heterogeneous Architectures (CAPA) aims to address the problem of effective software development for diverse hardware architectures through groundbreaking university research that will lead to a significant, measurable leap in software development productivity by partially or fully automating software development tasks that are currently performed by humans. The main research objectives for CAPA include programmer effectiveness, performance portability, and performance predictability. In order to address these objectives, CAPA seeks research proposals that explore (1) programming abstractions and/or methodologies that separate performance-related aspects of program design from how they are implemented; (2) program synthesis and machine learning approaches for automatic software construction that are demonstrably correct; (3) advanced hardware-based cost models and abstractions to support multi-target code generation and performance predictability for specified heterogeneous hardware architectures; and (4) integration of research results into principled software development practices.

Computing systems have undergone a fundamental transformation from the single-processor devices of the turn of the century to today's ubiquitous and networked devices and warehouse-scale computing via the cloud. Parallelism has become ubiquitous at many levels. The proliferation of multi- and many-core processors, ever-increasing numbers of interconnected high performance and data intensive edge devices, and the data centers servicing them, is enabling a new set of global applications with large economic and social impact. At the same time, semiconductor technology is facing fundamental physical limits and single processor performance has plateaued.  This means that the ability to achieve predictable performance improvements through improved processor technologies has ended. The Exploiting  Parallelism and Scalability (XPS) program aims to support groundbreaking research leading to a new era of parallel computing. XPS seeks research re-evaluating, and possibly re-designing, the traditional computer hardware and software stack for today's heterogeneous parallel and distributed systems and exploring new holistic approaches to parallelism and scalability.  Achieving the needed breakthroughs will require a collaborative effort among researchers representing all areas-- from the application layer down to the micro-architecture-- and will be built on new concepts and new foundational principles. New approaches to achieve scalable performance and usability need new abstract models and algorithms, programming models and languages, hardware architectures, compilers, operating systems and run-time systems, and exploit domain and application-specific knowledge. Research should also focus on energy- and communication

The goal of the National Robotics Initiative (NRI) is to support fundamental research that will accelerate the development and use of robots in the United States that work beside or cooperatively with people. The original NRI program focused on innovative robotics research that emphasized the realization of collaborative robots (co-robots) working in symbiotic relationships with human partners. The NRI-2.0 program significantly extends this theme to focus on issues of scalability: how teams of multiple robots and multiple humans can interact and collaborate effectively; how robots can be designed to facilitate achievement of a variety of tasks in a variety of environments, with minimal modification to the hardware and software; how robots can learn to perform more effectively and efficiently, using large pools of information from the cloud, other robots, and other people; and how the design of the robots’ hardware and software can facilitate large-scale, reliable operation

The goal of the National Robotics Initiative (NRI) is to support fundamental research that will accelerate the development and use of robots in the United States that work beside or cooperatively with people. The original NRI program focused on innovative robotics research that emphasized the realization of collaborative robots (co-robots) working in symbiotic relationships with human partners. The NRI-2.0 program significantly extends this theme to focus on issues of scalability: how teams of multiple robots and multiple humans can interact and collaborate effectively; how robots can be designed to facilitate achievement of a variety of tasks in a variety of environments, with minimal modification to the hardware and software; how robots can learn to perform more effectively and efficiently, using large pools of information from the cloud, other robots, and other people; and how the design of the robots' hardware and software can facilitate large-scale, reliable operation. In addition, the program supports innovative approaches to establish and infuse robotics into educational curricula, advance the robotics workforce through education pathways, and explore the social, behavioral, and economic implications of our future with ubiquitous collaborative robots. Collaboration between academic, industry, non-profit, and other organizations is encouraged to establish better linkages between fundamental science and engineering and technology development, deployment and use. Well-justified international collaborations that add significant value to the proposed research and education activities will also be considered.

The DoD strives for superiority in a complex operational environment composed of heterogeneous and ever-evolving pieces such as sensors and processing hardware, software, and wired and wireless communications infrastructure. To achieve superiority in this dynamic environment, the DoD invests in fully networked and maximally-capable sensors and platforms at the cutting edge of performance. Software has emerged as a key enabler of capability and flexibility in DoD systems. Software is essential to providing a vast range of military capabilities by playing a fundamental role [1] that deepens, broadens, links, and integrates diverse system elements. A pervasive DoD challenge is the production of software that provides the Warfighter superior and affordable military capability in an environment where hardware and sensor technology is rapidly advancing and systems' performance demands are ever-increasing. The Government Accountability Office has identified the increasing scope of software development as a contributing factor to poor acquisition program outcome [2]. OASD(R&E) has identified the need for advanced software engineering technology to enhance the producibility of software in this environment with the goal of speeding the delivery of affordable capability to the Warfighter and sustaining it.

Help create a sustainable, competitive, and healthy US food system. Use USDA data to create working, interactive applications to get farmers the information they need - and help feed America. What to Create: Submit a working, interactive application that integrates one or more of the required USDA datasets.  Static data visualizations will not be eligible. Applications must include interactive functionality (e.g. the user can change parameters to update the visualization and/or result). Eligible Platforms: Smartphone or tablet (iOS, Android, Blackberry, Kindle, Windows 8 Mobile) Web (mobile or desktop) Desktop (Windows PC, Mac Desktop) Software running on other publicly available hardware (including, but not exclusive to, wearable technology, open source hardware, etc.) Supplemental Material: You must submit a demo video (hosted on YouTube, Vimeo, or Youku) that walks through the main functionality of the application via screencast or video. You must also submit a text description and at least one image/screenshot of your working application. Testing: You must make your app available for testing by providing a link to access your installation file, an uploaded installation file, a beta distribution build, etc. See full testing access options. New & Existing Solutions: Apps may be newly created or pre-existing. If the submitted app existed prior to the competition's submission start date, it must have been updated to integrate the required USDA data during the submission period.

The Chan Zuckerberg Initiative (CZI) seeks to support up to 10 Imaging Scientists who will work at the interface of biology, microscopy hardware, and imaging software at imaging centers across the United States. "Imaging Scientists" might be engineers, physicists, mathematicians, computer scientists, or biologists who have focused on technology development in either microscopy or data analysis fields. The primary goal of the program is to increase interactions between biologists and technology experts. The Imaging Scientists will have expertise in microscopy hardware and/or imaging software. A successful "Imaging Program" will employ an Imaging Scientist who: a) works collaboratively with experimental biologists on projects at the imaging center; b) participates in courses that disseminate advanced microscopy methods and analysis; c) trains students and postdocs in imaging technology; d) participates in a network of CZI Imaging Scientists to identify needs and drive advances in the imaging field; e) attends twice-yearly CZI scientific workshops and meetings in imaging and adjacent biomedical areas. Each grant will fund salary and fringe benefits for an Imaging Scientist at the center, a modest travel and teaching budget, plus 15% indirect costs. The award period is three years plus an additional two years if the Imaging Program passes a review at year three.

This BAA is intended to solicit extramural research and development ideas, and is issued under the provisions of the Competition in Contracting Act of 1984 (Public Law 98-369), as implemented in Federal Acquisition Regulation 6.102(d) (2) and 35.016. This announcement provides a general description of USSOCOM's research areas of interest, general information, evaluation and selection criteria, and proposal/application preparation instructions. In accordance with FAR 6.102, projects funded under this announcement must be for basic and applied research and that part of development not related to the development of a specific system or hardware procurement. Projects must be for scientific study and experimentation directed toward advancing the state-of-the-art or increasing knowledge or understanding. Projects that are for the development of a specific system or hardware procurement will not be considered. The selection process is highly competitive and the quantity of meaningful proposal/applications (both pre-proposal/pre-applications and full proposal/full applications) typically received exceed the number of awards that available funding can support. This BAA provides a general description of USSOCOM's research and development programs, including research areas of interest, evaluation and selection criteria, pre-proposal/pre-application and full proposal/application preparation instructions, and general administrative information. Specific submission information and additional administrative requirements can be found in the document titled "General Submission Instructions" available in Grants.gov along with this BAA.

The Department of Energy's (DOE) Office of Advanced Scientific Computing Research (ASCR) announces its interest in receiving applications to explore of the suitability of various implementations of quantum computing hardware for science applications. This foundational research will facilitate the development of device architectures well-suited for scientific applications of quantum computing and improve our understanding of the advantages and limitations of various approaches to quantum computing for science applications. The purpose of this FOA is to invite applications for foundational research in the following two areas: 1. Exploring the relationship between device architecture and application performance 2. Developing meaningful metrics for evaluating the suitability of quantum computing hardware for science applications Applications may address one or both of these themes. Proposed research should focus on devices that are already available or that become available during the term of the award rather than large-scale, high-fidelity, fault-tolerant machines. Funded teams will be expected to collaborate externally with researchers working to develop applications and algorithms that can expand the frontiers of scientific discovery. Funded teams will also be expected to participate in community engagement activities that support the growth of an active, integrated research community committed to the common goal of developing quantum computing resources for advancing scientific discovery. Topics that are out of scope include: development and optimization of quantum algorithms; development of new candidate qubit systems; schemes based on qubits that have not yet demonstrated high-fidelity gates; schemes to improve the performance and functionality of qubits; quantum transduction; quantum communication, networking, and key distribution; cryptography and cryptanalysis; and logical qubits beyond considerations given to scaling to ~10 qubit devices.

This BAA is intended for industry and academia submissions and considerations related to advanced research technology development, innovative commercial technologies, development not related to the development of a specific system or hardware procurement. However, submissions may be directly relevant to enhancing mission effectiveness of military personnel and the supporting platforms, systems, components, or material proposed to be acquired or developed by the Department of Defense, or improvement of platforms, systems, components, or materials in use by the armed forces. Work funded under this BAA may include applied research and advanced technology development that may or may not be related to the development of a specific system or hardware procurement.

The program supports four main research thrusts that are envisioned to advance the goal of ubiquitous co-robots: scalability, customizability, lowering barriers to entry, and societal impact. Topics addressing scalability include how robots can collaborate effectively with multiple humans or other robots; how robots can perceive, plan, act, and learn in uncertain, real-world environments, especially in a distributed fashion; and how to facilitate large-scale, safe, robust and reliable operation of robots in complex environments. Customizability includes how to enable co-robots to adapt to specific tasks, environments, or people, with minimal modification to hardware and software; how robots can personalize their interactions with people; and how robots can communicate naturally with humans, both verbally and non-verbally. Topics in lowering barriers to entry should focus on lowering the barriers for conducting fundamental robotics research and research on integrated robotics application. This may include development of open-source co-robot hardware and software, as well as widely-accessible testbeds. Outreach or using robots in educational programs do not, by themselves, lower the barriers to entry for robotics research. Topics in societal impact include fundamental research to establish and infuse robotics into educational curricula, advance the robotics workforce through education pathways, and explore the social, economic, ethical, and legal implications of our future with ubiquitous collaborative robots.

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications, healthcare informatics and business analytics.

Hardware and/or software technologies to obtain the following kinds of information from a human face through cameras or other devices. Any types of measurement hardware including devices employing infrared, sonic, voice recognition or other technologies should be in the scope.

A key focus of the design of modern computing systems is performance and scalability, particularly in light of the limits of Moore's Law and Dennard scaling. To this end, systems are increasingly being implemented by composing heterogeneous computing components and continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid strides in machine learning, data analysis, and extreme-scale simulation are becoming more domain-specific and highly distributed. In this scenario, traditional boundaries between hardware-oriented and software-oriented disciplines increasingly are blurred. Achieving scalability of systems and applications will therefore require coordinated progress in multiple disciplines such as computer architecture, high-performance computing (HPC), programming languages and compilers, security and privacy, systems, theory, and algorithms. Cross-cutting concerns such as performance (including, but not limited to, time, space, and communication resource usage and energy efficiency), correctness and accuracy (including, but not limited to, emerging techniques for program analysis, testing, debugging, probabilistic reasoning and inference, and verification), security and privacy, robustness and reliability,  domain-specific design, and heterogeneity must be taken into account from the outset in all aspects of systems and application design and implementation. 

A key focus of the design of modern computing systems is performance and scalability, particularly in light of the limits of Moore's Law and Dennard scaling. To this end, systems are increasingly being implemented by composing heterogeneous computing components and continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid strides in machine learning, data analysis, and extreme-scale simulation are becoming more domain-specific and highly distributed. In this scenario, traditional boundaries between hardware-oriented and software-oriented disciplines increasingly are blurred. Achieving scalability of systems and applications will therefore require coordinated progress in multiple disciplines such as computer architecture, high-performance computing (HPC), programming languages and compilers, security and privacy, systems, theory, and algorithms. Cross-cutting concerns such as performance (including, but not limited to, time, space, and communication resource usage and energy efficiency), correctness and accuracy (including, but not limited to, emerging techniques for program analysis, testing, debugging, probabilistic reasoning and inference, and verification), security and privacy, robustness and reliability, domain-specific design, and heterogeneity must be taken into account from the outset in all aspects of systems and application design and implementation.

1. Lithium-ion Battery Safety. Safety concerns continue to hamper full adoption of lithium-ion batteries for defense systems, despite significant research investments by the government and the private sector. This Defense initiative will advance promising lithium-ion battery safety technologies at university research laboratories into early laboratory prototypes and potentially minimum viable products for adoption by the defense and commercial sectors via early startups, small businesses and non-traditional defense contractors. Specific technical areas of interest include, but are not limited to, the following: improved electrolytes; stable high-energy anodes and cathodes; cell components and structures that enhance safety and reliability (e.g. use of electrode coatings and electrolyte additives); safety optimization through battery and battery module design and packaging; and battery management and state of health techniques that prevent and/or mitigate catastrophic failure. 2. Electrical Grid Reliability, Resiliency and Security. Both the defense and commercial sectors recognize the ever-growing criticality to enhance electrical grid reliability, resiliency and security through innovation at the component and system levels. This Defense initiative will advance relevant electrical grid innovations at university research laboratories into early laboratory prototypes and potentially minimum viable products for adoption by the defense and commercial sectors via early startups, small businesses and non-traditional defense contractors. Specific technical areas of interest include, but are not limited to, the following: advanced electrical power generation, transmission and distribution hardware and software; physical cyber secured industrial controls hardware and software; effective control of microgrids supporting high-dynamic loads; electrical grid protocols and controls to maintain secured operations of critical infrastructure under adverse conditions; hardening of e

The program supports four main research thrusts that are envisioned to advance the goal of ubiquitous co-robots: scalability, customizability, lowering barriers to entry, and societal impact. Topics addressing scalability include how robots can collaborate effectively with multiple humans or other robots; how robots can perceive, plan, act, and learn in uncertain, real-world environments, especially in a distributed fashion; and how to facilitate large-scale, safe, robust and reliable operation of robots in complex environments. Customizability includes how to enable co-robots to adapt to specific tasks, environments, or people, with minimal modification to hardware and software; how robots can personalize their interactions with people; and how robots can communicate naturally with humans, both verbally and non-verbally. Topics in lowering barriers to entry include development of open-source co-robot hardware and software, as well as widely-accessible testbeds. Topics in societal impact include fundamental research to establish and infuse robotics into educational curricula, advance the robotics workforce through education pathways, and explore the social, economic, ethical, and legal implications of our future with ubiquitous collaborative robots.