FY2023 NITRD Program Component Areas (PCAs)

The FY2023 PCAs described on this page are those used by NITRD agencies in compiling the PCA budget information for the NITRD Supplement to the President’s FY2023 Budget.

 

FY2023 Changes to the NITRD PCAs

  • Updated PCA definitions
  • Updated the LSN — Large-Scale Networking PCA to ACNS – Advanced Communication Networks and Systems PCA

 

IWG-to-PCA Mapping for the FY2023 Supplement

For each annual NITRD Supplement, agencies “map” their NITRD Program activities, coordinated by Interagency Working Groups (IWGs), into Supplement sections and budgets organized by PCAs. The FY2023 IWG to PCA mapping by agencies is available by: Graphic | Text.
 

FY2023 NITRD PCA List

 

FY2023 NITRD PCA Definitions

ACNS – Advanced Communication Networks and Systems (formerly LSN—Large-Scale Networking)
ACNS R&D advances and validates communication networks and systems, including wireless, optical, or quantum communication technologies and services; this includes R&D in networking architectures, programmability, security, measurement, performance, robustness, resilience, and interoperability, along with techniques for advancing spectrum efficiency.

[Sub-PCA] Advanced Wireless R&D includes Federal spectrum-related R&D investments that promote efficient use of wireless spectrum through advanced technologies and systems.

↑ PCA List 
 

AI – Artificial Intelligence *
AI R&D advances the technical capabilities of computational systems to conduct, simulate, or extend the performance of tasks that have traditionally required human intelligence; this includes innovations in perception (to include spoken language and gestures), computer vision, natural language technologies, representation, learning, reasoning, recommendation, and action; novel and use-inspired application of these techniques to various domains; and examination of trustworthiness and the associated measurements, methods, and tools needed for designing, developing, and evaluating such systems.
↑ PCA List 
 
CHuman – Computing-Enabled Human Interaction, Communication, and Augmentation
CHuman R&D advances the ability of individuals to interact with one another and with computing, communication, and information technologies; this includes R&D of human-to-human and human-to-machine interactions and collaborations, and the impacts on society.
↑ PCA List 
 
CNPS – Computing-Enabled Networked Physical Systems
CNPS R&D advances systems that are complex, highly-reliable, real-time, networked, and/or hybrid; this includes R&D in cyber-physical systems and the Internet of Things.
↑ PCA List 
 
CSP – Cyber Security and Privacy
CSP R&D advances the security and privacy of computing, communication, and information technologies; this includes R&D on how human behavior and usability interact with technical aspects of cybersecurity and privacy.
↑ PCA List 
 
EdW – Education and Workforce
EdW R&D advances the use of computing, communication, and information technologies to enhance education and workforce training at all levels; this includes the recruitment, preparation, and retention of a diverse population of researchers, entrepreneurs, and users; and support for learning, teaching, assessment, standards, and virtual education and training.
↑ PCA List 
 
ENIT – Electronics for Networking and Information Technology (new in FY2022)
ENIT R&D advances micro- and nanoelectronics design, architecture, validation, and testing across the networking and information technology hardware design stack; this includes methodologies for scalable and energy-efficient systems, silicon and/or non-silicon technologies, and implementations in computing and communication architectures.
↑ PCA List 
 
EHCS – Enabling-R&D for High-Capability Computing Systems
EHCS R&D advances and translates new approaches in high-capability computing; this includes R&D in novel computing paradigms, hardware architectures, algorithms, software, data analytics, system performance, reliability, trust, transparency, energy efficiency, and other methods that enable extreme data- and compute-intensive workloads.
↑ PCA List 
 
HCIA – High-Capability Computing Infrastructure and Applications
HCIA provides the operation, integration, and utilization of high-capability computing systems and infrastructure supporting computation-intensive and data-intensive application workflows; this includes software and services, communications, storage, and data infrastructure, coordination services, and other necessary resources for the effective use of high-capability computing.
↑ PCA List 
 
IRAS – Intelligent Robotics and Autonomous Systems
IRAS R&D advances intelligent robotic systems that are increasingly autonomous; this includes R&D in robotics hardware and software design and application, machine perception, cognition and adaptation, mobility and manipulation, safe human-robot interaction, and distributed and networked robotics.
↑ PCA List 
 
LSDMA – Large-Scale Data Management and Analysis
LSDMA R&D advances the ecosystem needed for extraction of knowledge and insights from data; this includes R&D in the capture, curation, provenance, privacy preservation, management, governance, access, analysis, reusability, and presentation of large-scale and diverse data.
↑ PCA List 
 
SPSQ – Software Productivity, Sustainability, and Quality
SPSQ R&D advances timely and affordable development and sustainability of low-defect, low-vulnerability software; this includes R&D to improve software development productivity, quality, measurement, assurance, and adaptability while also providing essential characteristics such as security, privacy, usability, and reliability.
↑ PCA List 
 

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* Please note that we understand R&D in AI will intersect with multiple PCAs. For example:

  • R&D on general methods for machine vision would fall under AI, while R&D on robots, even if the robots employ machine vision, would fall under IRAS. Note that R&D on intelligent autonomous systems that exist only in cyberspace, with no physical embodiment, would be reported under AI.
  • R&D on algorithms for computational linguistics would fall under AI, while R&D on the broad problem of human-machine interaction, even if it contains an element of natural language processing, would fall under CHuman.
  • R&D on the cybersecurity challenges unique to AI, such as the ability to exploit flaws in an AI system’s goals would fall under AI, whereas AI supporting cybersecurity research would fall under CSP.
  • R&D on special neuromorphic computing architectures or chips optimized for neural nets would fall under AI, whereas general research in neuromorphic computing would fall under EHCS.
  • R&D that is primarily machine learning would fall under AI, while R&D on the larger data management and analysis ecosystem, even if it contains an element of machine learning, would fall under LSDMA.

Agencies should consider these examples and report an activity under the PCA that is most specific to that activity.