FY2022 NITRD Program Component Areas (PCAs)

The FY2022 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 FY2022 Budget.


IWG-to-PCA Mapping for the FY2022 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 FY2022 IWG to PCA mapping by agencies is available by: Graphic | Text.



FY2022 NITRD PCA Definitions

AI – Artificial Intelligence *
AI R&D advances the ability of computer systems to perform tasks that have traditionally required human intelligence; this includes R&D in machine learning, computer vision, natural language processing/understanding, intelligent decision support systems, and autonomous systems, as well as the novel application of these techniques to various domains, where not principally covered by other PCAs.
↑ PCA List 
CHuman – Computing-Enabled Human Interaction, Communication, and Augmentation
CHuman R&D advances information technologies that enhance people’s ability to interact with IT systems, other people, and the physical world; this includes R&D in social computing, human-human and human-machine interaction and collaboration, and human and social impacts of IT.
↑ PCA List 
CNPS – Computing-Enabled Networked Physical Systems
CNPS R&D advances information technology-enabled systems that integrate the cyber/information, physical, and human worlds; this includes R&D of cyber-physical systems, Internet of Things, and related complex, high-reliability, real-time, networked, and hybrid computing and engineered systems.
↑ PCA List 
CSP – Cyber Security and Privacy
CSP R&D advances protection of information, information systems, and people from cyber threats and prevention of adverse privacy effects arising from information processing; this includes R&D to deter, detect, prevent, counter, respond to, recover from, and adapt to threats to the availability, integrity, and confidentiality of information and information systems, along with R&D to address privacy goals of individuals and society related to direct and indirect effects of information processing.
↑ PCA List 
EdW – Education and Workforce
EdW R&D advances use of information technology to improve education and training; this includes IT to enhance learning, teaching, assessment, and standards, as well as preparation of next-generation cyber-capable citizens and professionals.
↑ PCA List 
ENIT – Electronics for Networking and Information Technology
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 energy-efficient systems, silicon and/or non-silicon technologies, and implementations in computing and communications architectures.
↑ PCA List 
EHCS – Enabling-R&D for High-Capability Computing Systems
EHCS R&D advances high-capability computing and development of fundamentally new approaches in high-capability computing; this includes R&D in hardware and hardware subsystems, software, architectures, system performance, computational algorithms, data analytics, development tools, and software methods for extreme data- and compute-intensive workloads.
↑ PCA List 
HCIA – High-Capability Computing Infrastructure and Applications
HCIA investments advance operation and utilization of systems and infrastructure for high- capability computing, including computation- and data-intensive systems and applications; directly associated software, communications, storage, and data management infrastructure; and other resources supporting high-capability computing.
↑ PCA List 
IRAS – Intelligent Robotics and Autonomous Systems
IRAS R&D advances intelligent robotic systems; this includes R&D in robotics hardware and software design and application, machine perception, cognition and adaptation, mobility and manipulation, human-robot interaction, distributed and networked robotics, and increasingly autonomous systems.
↑ PCA List 
LSDMA – Large-Scale Data Management and Analysis
LSDMA R&D advances extraction of knowledge and insights from data; this includes R&D of the capture, curation, provenance, management, access, analysis, and presentation of large, diverse, often multisource, data.
↑ PCA List 
LSN – Large-Scale Networking
LSN R&D advances networking technologies and services; this includes R&D in networking architectures, wireless networks, software-defined networks, heterogeneous multimedia networks, testbeds, grid and cloud research and infrastructure, network service and cloud computing middleware, identity management, and end-to-end performance enhancement and performance measurement.

[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. This covers the R&D that is coordinated through the NITRD Wireless Spectrum R&D Interagency Working Group.

↑ PCA List 

SPSQ – Software Productivity, Sustainability, and Quality
SPSQ R&D advances timely and affordable development and sustainment of low-defect, low-vulnerability software; this includes R&D to significantly improve software production processes; productivity, quality, and understanding of the economics of software and its development, sustainability, measurement, assurance, and adaptability; and guarantees of essential requirements such as security, privacy, usability, reliability, and autonomy.
↑ PCA List 


* 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 the AI PCA, 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 that is primarily machine learning would fall under the AI PCA, while R&D on the larger data management and analysis ecosystem, even if it contains an element of machine learning, would fall under LSDMA.
  • 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.

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