FOA-AFRL-AFOSR-2023-0012 Amendment 0002.pdf

FUNDING OPPORTUNITY ANNOUNCEMENT

OVERVIEW INFORMATION

This Funding Opportunity Announcement (FOA) describes a newly proposed initiative of the Air Force Research Laboratory (AFRL) concerning the University Center of Excellence (COE) in “Extreme Neuromorphic Materials and Computing.” The University COE is defined as a joint effort among Air Force Office of Scientific Research (AFOSR), Air Force Research Laboratory Technology Directorates (AFRL TDs), and an outstanding university team to perform high-priority collaborative research which addresses research objectives of the United States Air Force (USAF) and the United States Space Force (USSF). The proposed COE is a joint project between AFOSR, AFRL Information Directorate (AFRL/RI), and AFRL Materials and Manufacturing Directorate (AFRL/RX).

AFOSR anticipates making at least one grant award of up to $1,000,000 per year per award, for a maximum of five years. The base period is for three years, which a consortium of AFOSR, AFRL/RI, and AFRL/RX intends to incrementally fund, followed by an option to extend an additional two years provided the COE passes a midterm review. All funding decisions are at the Government’s discretion and are subject to the availability of funds.

Proposers are highly encouraged to confer with the designated AFOSR Program Officers as soon as possible. AFOSR will evaluate proposals relying on a peer review panel and the criteria specified in section E: “Application Review Information.” AFOSR will not provide funding for reimbursement of proposal or application costs associated with responding to this FOA.

White Papers briefly summarizing the proposing institution’s ideas are highly encouraged. The AFOSR Program Officers will coordinate with the Technical Representatives of the sponsoring AFRL TDs, i.e., AFRL/RI and AFRL/RX, to provide feedback to white papers and will share responsibility for ensuring the success of a COE.

Proposals may choose to include a data management plan that outlines how samples and data collected in the program will be stored and managed. This includes, but is not restricted to, issues such as: standards for data and metadata collection, content and format, data archiving, database management, and data sharing within and outside the COE. This is modeled on the National Science Foundation Data Management Plan.

Hyperlinks have been embedded within this document and appear as underlined, and or blue-colored words in the midst of paragraphs. The reader may “jump” to the linked section within this document by “clicking” (CTRL + CLICK, or CLICK).

SUMMARY FUNDING OPPORTUNITY INFORMATION

1. FEDERAL AWARDING AGENCY NAME

Air Force Office of Scientific Research 875 North Randolph Street, STE 325, Room 3112 Arlington, VA 22203

2. FUNDING OPPORTUNITY TITLE

CENTER OF EXCELLENCE (COE): Extreme Neuromorphic Materials and Computing

3. ANNOUNCEMENT TYPE

Amendment

4. ANNOUNCEMENT NUMBER

FOA-AFRL-AFOSR-2023-0012 Amendment 0002

5. CATALOG OF FEDERAL DOMESTIC ASSISTANCE (CFDA) NUMBER

12.800 Air Force Defense Research Sciences Program

6. KEY DATES

Pre-proposal inquiries and questions must be received in writing by electronic mail not later than 29 June 2023 at 11:59 PM Eastern Time (ET) to be considered.

White papers are highly encouraged and must be submitted electronically at https://community.apan.org/wg/afosr/p/submitawhitepaper by 20 July 2023 at 11:59 PM Eastern Time to be considered. White paper evaluation is meant to initially assess the capability of a proposed effort and is NOT a selection process. White papers can be up to 8 pages in length plus references. White papers should adequately articulate:

1. An initial list of members of the proposed team with a short bio or CV for each (CVs and/or bios will not count against page limit).

2. The main technical components of the proposed research and how it aligns with the goals of the solicitation.

3. The specific proposed activities to establish a relationship with AFRL/RI, AFRL/RX, and

AFOSR.

4. The specific plans to educate students/postdoctoral fellows, expose them to research opportunities with AFRL TDs, and ultimately further the interests of the USAF/USSF.

The Government will respond to white papers before COB on 3 August 2023.

Proposals must be received electronically through Grants.gov by 28 August 2023 at 11:59 PM Eastern Time to be considered.

We anticipate that the Government will notify proposers of selection or non-selection for award by 8 November 2023.

https://community.apan.org/wg/afosr/p/submitawhitepaper

TABLE OF CONTENTS

A. PROGRAM DESCRIPTION

1. SUMMARY

2. BACKGROUND

3. OBJECTIVES AND RESEARCH CONCENTRATION AREAS

4. UNIVERSITY CENTER OF EXCELLENCE

B. FEDERAL AWARD INFORMATION

C. ELIGIBILITY INFORMATION

1. ELIGIBLE APPLICANTS

a. Qualified and Responsible United States Educational Institutions

b. HBCU/MI, Tribal College and University Applicants Encouraged

c. Ineligible Entities

2. COST SHARING OR MATCHING

3. OTHER

a. Research Personnel Facility Access Requirements and Restrictions

b. Acknowledgment of Support and Disclaimer Requirements

c. Expectation of Public Dissemination of Research Results

d. Conflict of Interest

D. APPLICATION AND SUBMISSION INFORMATION

1. ADDRESS TO REQUEST APPLICATION PACKAGE

2. CONTENT AND FORM OF APPLICATION SUBMISSION

a. Pre-proposal Inquiries and Questions

b. The Application as a Whole

c. Proposal Format

d. Proposal Length

e. No Confidential or Proprietary Information

f. Electronic Form and Proposal Attachments

3. GRANTS.GOV APPLICATION SUBMISSION AND RECEIPT PROCEDURES

a. Electronic Delivery

b. How to Register to Apply through Grants.gov

c. How to Submit an Application to AFOSR via Grants.gov

4. COMPONENT PIECES OF THE APPLICATION

a. SF 424 (R&R) Application for Federal Assistance

b. SFLLL Disclosure of Lobbying Activities Form

c. Certification Regarding Lobbying Form

d. R&R Other Project Information Form

e. R&R Publicly Releasable Project Summary / Abstract

f. Project Narrative

g. Bibliography and References Cited

h. R&R Senior / Key Person Profile Form

i. R&R Budget Form

j. R&R Sub award Budget Form

k. Budget Justification

l. R&R Project / Performance Site Locations Form

m. Data Management Plan (Required)…………………………………………………………33

n. R&R Personal Data Form…………………………………………………………………..33

5. INFORMATION YOU MUST SUBMIT IF SELECTED FOR POSSIBLE AWARD

6. UNIQUE ENTITY IDENTIFIER (UEI), CAGE, AND SYSTEM FOR AWARD

MANAGEMENT (SAM)

a. SAM Registration Required

b. SAM Exemption or Exceptions Not Available Under This Announcement

c. Questions about SAM Registrations and Updates

d. Consequences of Non-Compliance with SAM Registration Requirements

7. SUBMISSION DATES AND TIMES

a. Pre-Proposal Inquiries and Questions Deadline

b. How Submission Time for Pre-Proposal Inquiries and Questions is Determined

c. Effect of Missing Pre-Proposal Inquiries and Questions Deadline

d. Proposal Submission Deadline

e. How Proposal Submission Time is Determined

f. Grants.gov Tracking Number is Application Receipt

g. Effect of Missing the Proposal Submission Deadline

8. INTERGOVERNMENTAL REVIEW

9. FUNDING RESTRICTIONS

a. Proposal Preparation Costs

b. Air Force Office of Scientific Research No-cost Extension (NCE) Policy

c. Prohibition on Contracting with Entities that Require Certain Internal Confidentiality Agreements or Statements--Representation

d. Other Submission Requirements

E. APPLICATION REVIEW INFORMATION

1. CRITERIA

a. Principal Evaluation and Selection Criteria

b. Additional Evaluation and Selection Criteria

2. REVIEW AND SELECTION PROCESS

a. Merit-based, Competitive Procedures

b. Cost Analysis for Reasonableness and Realism

3. DISCLOSURE OF ADMINISTRATIVE PROCESSING BY CONTRACTOR PERSONNEL

4. NO GUARANTEED AWARD

F. FEDERAL AWARD ADMINISTRATION INFORMATION

1. SELECTION NOTICES

a. Electronic Notification

b. Selection for Possible Award Does Not Authorize Work

2. AWARD NOTICIES

a. Federal Award Document

b. Electronic Federal Award Distribution

3. ADMINISTRATIVE AND NATIONAL POLICY REQUIREMENTS

a. Reporting of Matters Related to Recipient Integrity and Performance

b. Agency Review of Risk Posed by Applicants

c. Cross-Cutting National Policy Requirements

d. Acknowledgment of Research Support

e. Disclaimer Language for Research Materials and Publications

f. Uniform Administrative Requirements, Cost Principles, and Audit Requirements

g. DoD Research and Development General Terms and Conditions

h. Conditions of Award for Recipients Other Than Individuals

i. Minimum Record Retention Requirements

4. REPORTING

a. Monitoring and Reporting Program Performance

b. Technical Performance Report Format

c. Department of Defense (DD) Form 882 Report of Inventions and Subcontracts

d. Standard Form (SF) 425 Federal Financial Report

e. Electronic Payment Requests and Electronic Payment

f. Property Reports

g. Other Reports

h. Electronic Submission of Reports

G. AGENCY CONTACTS

1. TECHNICAL INQUIRES AND QUESTIONS

2. GENERAL INQUIRIES AND QUESTIONS

H. OTHER INFORMATION

1. OMBUDSMAN

2. GRANTS AND CONTRACTING OFFICERS AUTHORITY

3. ADDITIONAL FUNDING OPPORTUNITIES

A. PROGRAM DESCRIPTION

1. SUMMARY

The Air Force Office of Scientific Research (AFOSR) seeks unclassified proposals from educational institutions in the United States for the University Center of Excellence (COE) in “Extreme Neuromorphic Materials and Computing.”

The University COE is defined as a joint effort among AFOSR, Air Force Research Laboratory Technology Directorates (AFRL TDs), and an outstanding university or team of universities to perform high-priority collaborative research. The proposed COE is a joint project between AFOSR, AFRL Information Directorate (AFRL/RI), and AFRL Materials and Manufacturing Directorate (AFRL/RX), referred to collectively as “we, our, or us” or “AFRL” in this announcement. This COE is anticipated to extend the research interests and capabilities of AFRL on the subject area and provide opportunities for a new generation of US scientists and engineers to address United States Air Force (USAF) and United States Space Force (USSF) research needs. Proposals must not contain any proprietary information.

We will consider proposals for up to five (5) years with a three-year (3) base period and a two-year (2) option period.

We will evaluate proposals relying on a peer review panel organized in consultation with the Technical Representatives of AFRL TDs and the criteria specified in section E: “Application Review Information.”

We anticipate making at least one award of up to $1,000,000 per year per award, for a maximum of five years, under this competition for a team proposal with no more than five universities, supporting up to seven faculty researchers with 90% of total budget allocated for the support of no more than six core faculty researchers.

AFRL invites proposals for research in the areas described in detail below. The schedule for this announcement is given in Section B, Federal Award Information. This research effort will consist of multidisciplinary teams of researchers with the skills needed to address the relevant research challenges necessary to meet the program’s goals. Multi-investigator teaming is encouraged with up to seven faculty researchers including no more than six core members supported by 90% of total budget. University teaming (up to five) is allowed. Under no circumstances will the Government help to create teams.

2. BACKGROUND

In DoD missions, the exponential growth of data demands advanced data analysis capabilities with higher processing performance, lower energy dissipation, and better system scalability.

During the past twenty years, the artificial intelligence (AI) and data processing computation demand has doubled every 3.5 months while performance of the processors has doubled roughly only every 3.5 years (and likely not continue due to the end of Moore’s Law). In addition, the intrinsic limitations of von Neumann architecture based on CMOS technology are prohibiting our computing platforms from meeting future AI and autonomy computation requirements. These limitations have motivated emerging research areas in neuromorphic computing inspired by the architecture and working mechanism of the human brain, which is widely recognized as the ultimate computing engine with extremely high energy efficiency, reliable performance, efficient learning, and robust cognitive abilities.

The human brain’s cognitive functions emerge from the collective processing capability of simple computing elements, i.e., neurons, synapses, and dendrites. In such biological neural networks, spike sequences carry both spatial and temporal information for communication and processing. Moreover, neurons operate asynchronously in an event-driven manner, and biological neural networks demonstrate very energy-efficient information processing. Such biological neural networks have initially been simulated via software-only approaches, and the scale of simulated networks usually is small due to high communication overhead and limited parallelizable computing with conventional hardware. The artificial neural network (ANN) approach loosely models neuron functionality and the massive connection of neurons in a biological brain, but ANN ignores a lot of essential features of biological neural networks.

Such a simplification makes the training process quite subtle, inefficient, and sensitive.

Although ANNs have obtained substantial successes in many applications such as image and speech recognition, the incredibly high computing cost required by training and poor support of in-hardware learning emerge as the main obstacles.

Neuromorphic models differ from their ANN counterparts in that they encode information via the temporal activation of neurons and precise emission of spikes. Over the past few years, many research efforts have been devoted to neuromorphic models to harness their higher computational potential. These efforts have focused on developing algorithms such as Widrow-Hoff type supervised plasticity rules for precise temporal spike-train recognition, back-propagation approximations for powerful data-driven learning, and spike-timing-dependent plasticity (STDP) for scalable temporal learning in progressively deeper neural architectures. CMOS based hardware has been built to implement the neuromorphic models and shown limited architectural design perspectives. The most visible examples are IBM’s TrueNorth neurosynaptic processor and Intel’s Loihi neuromorphic chip. CMOS devices and circuits, as the building elements for these hardware systems, were not created or optimized for neuromorphic computing purposes in the first place. Therefore, the existing approaches are not able to address the following deep scientific and technological gaps:

Gap 1: Next generation bio-inspired materials with neuromorphic dynamics. Bio-inspired materials that can fundamentally enable bio-realistic algorithms and models are still under-researched despite progress with metal oxides, polymer composites, photonics and biomolecules under the DARPA SyNAPSE program, and recent AFOSR Multidisciplinary University Research Initiative (MURI) programs. When relating these materials to CMOS devices, it is important to recognize that CMOS technologies were created for logic operation and arithmetic computation, and thus their dynamics are not well matched with the dynamics that are critical for neuromorphic computing. The dynamics of neurons, synapses and dendrites come from ion diffusion, which is not present in CMOS devices. Consequently, CMOS-based synapses and neurons require complex and bulky circuits built with transistors. Compared with the 10 μm2 neuron area, 0.001 μm2 synaptic area, and ~2 fJ synaptic operation energy in biological systems, the CMOS-based elements are 20 times, 400 times and 2,000 times larger, respectively. Novel intelligent materials, such as memristive materials providing ion diffusion dynamics, could offer such desirable dynamics to implement bio-inspired algorithms and models efficiently.

Gap 2: Neuromorphic dynamics and robustness in extreme environments. Extending the above-described efficient computational capability in extreme environments is critical for numerous DoD applications, from sensing and decision making in UAVs to long term operations of satellites. The direct exposure of edge computing devices to challenging operational environment makes them susceptible to corrosive, erosive, and high-temperature environments, limiting their practical applications. Therefore, it is timely and of great dividend to accelerate materials development and optimize reliability and durability of materials and devices for harsh environment applications. Such understanding will provide criteria and guidance for new material investigations.

Gap 3: Innovative circuits to serve as required computing elements. The bio-inspired intelligent materials need to be built into new circuits as required computing elements that can implement the bio-realistic algorithms and models. These circuits do not need be strictly electronic, and may incorporate materials with favorable dynamics in electronic, photonic, and microwave circuits, and may also be hybrids of different modalities. Different from the traditional linear circuits built with CMOS devices, bio-realistic circuit designs should be highly nonlinear and rich in dynamics, which present great opportunities and challenges in unconventional circuit designs. There is a lack of research to take advantage of the intrinsic nonlinear dynamics in designing novel circuit elements to efficiently and faithfully emulate synapses, neurons, and dendrites.

Gap 4: Scalable computing architecture for real world applications. Even with more capable and suitable devices and circuits, there is a lack of research and existing knowledge on how to construct a scalable architecture for real-world problems. It requires co-designs and co-optimizations across different layers to incorporate the desired properties of the new materials and circuits to realize an efficient, reliable, and scalable computing system. Taking reliability in extreme environments as an example, the stochastic nature of spike patterns is an important dynamical feature of robust neuron functions. A spiking-based model without stochastic elements will have not only limited performance potentials in probabilistic inference related applications but also lower resiliency to extreme external conditions and robust performance in real-world applications. The neuromorphic dynamics of the new materials would be intrinsically stochastic and can serve as the stochastic elements to solve this issue if appropriately incorporated into the new architecture designs.

Gap 5: Methods to realize bio-realistic algorithms and models. The algorithms and models used in biological neural networks (and their co-evolution with the underlying “wetware”) are likely why the brain is so efficient in performing many tasks such as learning. The back-propagation learning in a deep neural network (DNN) model requires a substantial amount of high-precision computing and memory, which are not affordable, especially for edge and Internet of Things (IoT) devices with limited resources. Therefore, most of the existing architectures only support inferencing functions of pre-trained neural networks that may not function well in real environments with dynamic data flows and characteristics because they cannot adapt to changes. State-of-the-art bio-plausible model research outcomes will need to be fully leveraged to identify key computing elements and neuromorphic dynamics that can guide novel materials and circuits research.

Focusing on overcoming the science and technology gaps 1, 2, and 3 mentioned above, this COE will investigate, discover and design (1) revolutionary brain-inspired materials and devices, (2) robust neuromorphic materials in extreme environments, and (3) more compact and capable computing elements; while continuously incorporating state-of-the-art research outcomes in scalable and reliable architectures and bio-plausible algorithms. It will also foster close collaboration with faculty members (to open pipelines for continuing research and strengthening in-house expertise), as well as their graduate students (as a means of recruiting talents), from top universities.

3. OBJECTIVES AND RESEARCH CONCENTRATION AREAS

This COE aims at high-risk, high-reward basic research that will address the hardest challenges currently facing neuromorphic computing. Specifically, the basic research objectives of this COE include: (1) Discover materials and devices with dynamics to enable bio-realistic algorithms and models; (2) Discover and design materials resistant to extreme environments; (3) Design innovative circuits based on neuromorphic materials to realize computing elements required by brain-inspired algorithms and models. In addition, this COE will leverage outside research to (1) Incorporate scalable, reliable architectures for bio-realistic algorithms and models and (2) Understand bio-realistic algorithms and models to identify key computing elements and neuromorphic dynamics.

Research Objective 1: Existing research and demonstrations of computing with memristive materials and devices are mostly limited to using their steady-state (static) behaviors for learning and inference. However, dynamics of ion motion in a biological neural network play a pivotal role in implementing neuroscience principles for the brain, which represents the most efficient and intelligent computing system ever known. Such dynamic properties can naturally encode temporal information and are highly desirable for implementing bio-realistic neural networks for applications such as time sequence prediction and natural language understanding. As with the corresponding biological components, memristive materials function based on particle motion. This makes it possible for memristive materials to generate dynamics like those in biological systems, leading to materials capable of implementing advanced learning and memory functions.

The emphasis of biological ion dynamics here requires new memristive materials to be different from the existing ones used for static, non-volatile memories. More specifically, both the mobile species and materials that host the mobile species need to be discovered for memristive materials with tunable activation energies and a variety of particle motion dynamics. Different from previous studies for steady-state properties, where only the static resistance states are relevant, the entire temporal switching process, i.e., the switching dynamics, should be utilized for computing and needs to be fully investigated and modeled. This imposes much greater challenges in characterizing, understanding, and modeling the memristive materials. For example, novel in-situ material characterization methods with very high temporal and spatial resolutions may be needed to reveal the microscopic picture of element migrations.

Research Objective 2: The direct exposure of edge computing devices to challenging operational environment makes them susceptible to corrosive, erosive and high-temperature environments, limiting their practical applications. One major challenge for memristors in harsh environments is performance degradation caused by high temperature and/or radiation. In addition, a significantly crucial application of memristors is the realization of artificial synapses for brain-inspired computing, where the radiation and other extreme environmental effects have remained unexplored. Understanding the materials degradation under extreme environments on memristor performance metrics would enable us in designing memristive materials which are resistant to extreme environments.

Research Objective 3: The rich dynamics of new materials may enable different types of neuromorphic devices and circuits, implemented in a variety of circuit modalities. For instance, the delay time and relaxation time of a volatile devices are important to realize a faithful artificial neuron and synapse, respectively. Concepts from the random recurrent neural network (RNN) and reservoir computing communities may offer alternative paths to exploit the inherent nonlinear dynamics of neuromorphic circuitry and devices.

Reservoir computing is a powerful machine learning method based on RNNs that use topological diversity and nonlinearity in dynamical processes to perform computational mappings. Instead of tuning the weights throughout the entire RNN to achieve a target mapping (an expensive and numerically unstable process), only the readout layer of the network is trained, using inexpensive linear regression methods. In addition, the diversity requirement of the network properties may inherently exploit variations in fabrication and operation of experimental systems. These advantages open the door to embedding RNN-like functionality in unconventional physics and hardware, allowing a reservoir to directly serve as the sensor platform and data processor at the edge of a system. Exploring how reservoir computing concepts can be integrated and complement bio-inspired, neuromorphic approaches is a potential, additional direction for this objective. Finally, dynamical devices that have large fanout and can be readily stacked to realize 3D circuit designs, which are necessary for a complex computing system with massive connectivity and computing, would be a great advantage.

Leverage Outside Research 1: A biological neural network is a complex system with rich dynamics that maps signals in time and space. Their efficiency can be attributed to the biological mechanisms of the neuron, dendrite, and synapse dedicated to these tasks.

Realizing the same dynamics in a circuit is a more complicated ordeal. As mentioned before, current silicon implementations of neurosynaptic dynamics involve intricate circuit topologies composed of multiple devices. However, these circuits require large amounts of chip area, consume a lot of power, and are more susceptible to noise and device variation.

The COE shall leverage circuit and architecture solutions that have a low complexity but can faithfully emulate the complex dynamics involved in biologically-plausible neurosynaptic computation and learning. Such hardware will take advantage of novel neuronal, dendritic and synaptic circuits that are able to capture and store temporal information as well as modulate spiking voltage inputs in a predictable way. It could mimic the behavior in a biological circuit, such as in some models where the timing relationships of pre-synaptic and post-synaptic spikes alter the weight of a synapse, modulating the effect of the pre-synaptic neuron on the post-synaptic neuron. Auxiliary circuits and architectures would be necessary to leverage these devices properly, enable system scaling, and virtually integrate with algorithms and applications. For example, given a network of novel neuron and synaptic circuits, a recurrent spiking neural network architecture would require auxiliary circuits for denoising, encoding, and decoding spiking data, a methodology for storing spike times, and a pipeline or schedule for transferring the data between layers. At the system level, predictable, replicable, and robust function and performance are expected. Close dialogue and co-design of materials, devices, circuits, architectures, and algorithms are important.

Leverage Outside Research 2: It is well established that neurons communicate information via “spikes” (or action potentials). Spiking neural networks (SNNs) are increasingly common algorithms that use simulated spikes to encode and communicate information and attempt to mimic the signals found in biological brains. The use of spikes is not only biologically-inspired but also improves the overall efficiency of the model. However, debates continue among neuroscientists regarding whether neurons use precise spike timing or frequency to encode information, the functions of noisy, probabilistic population codes, and whether every spike carries true information. Effective information representation is expected to correlate with the neuromorphic dynamics and the corresponding circuit scheme based on intelligent materials. It is clear that a biologically-plausible substitute for back-propagation is needed. Such a substitute would ideally be a multi-layer algorithm capable of training SNNs constructed of various types of connections and data representations that supports both supervised and unsupervised learning.

The COE shall leverage and incorporate the algorithms that are not only biologically realistic but also efficient to realize in hardware. An efficient learning rule is likely to take advantage of the dynamics of the neurons and synaptic circuits implemented in developed technologies. Bio-inspired systems are poised to excel in continuous time, thinking-with-context applications, such as processing sensory information and navigating in the real-world environment. During and beyond this COE, the ultimate research objective is to create processing and learning algorithms and systems that can adapt to time-varying contexts and environments by leveraging combined spatial and temporal information.

4. UNIVERSITY CENTER OF EXCELLENCE

Proposals for this multidisciplinary COE are sought that articulate the technical details of the proposed research and the planned mechanisms to educate a new generation of professionals.

This COE is focused on the fundamental understanding of two interrelated scientific challenges concerning (a) the discovery of new intelligent multifunctional materials allowing robust neuromorphic dynamics in extreme environments and (b) more effective multi-disciplinary design of the neuromorphic computing devices emulating the biological systems of synapses, neurons, and dendrites in terms of learning abilities, energy efficiency, and reliable performance. In implementing the COE, multi-investigator teaming across departments and disciplines is highly encouraged with a particular emphasis on transformative concepts over incremental efficiency gains. Developing students with cross-expertise and the ability to effectively communicate between these technical communities is paramount.

We expect researchers from the COE to work collaboratively with the AFRL researchers to address critical USAF and USSF challenges. The COE researchers will coordinate research projects with AFRL researchers, share information and best practices, author joint studies and publications, and provide a number of learning opportunities and applications for students and the defense community overall. Regular interactions, joint conferences, workshops, and other research exchange activities are expected. AFRL is open to leveraging the existing resources at AFRL TDs as well as AFOSR, including National Research Council (NRC) Summer Faculty Fellowship and Post-Doctoral programs. In this manner, educational institution staff and students with the appropriate credentials can be hosted at AFRL with minimal additional administrative overhead, in a coordinated fashion with the existing visiting faculty and summer research programs. Applicants are encouraged to consider providing personnel exchange opportunities that can foster AFRL activities that parallel graduate research groups at universities, i.e., closely integrated researchers under research advisors geared toward journal and conference paper publication and thesis/dissertation accomplishment.

AFRL is seeking both traditional and innovative approaches that strengthen and result in long term research collaborations with academia. Described below is what a strong proposal should provide.

4.1 Technical Details of Proposed Research

A strong proposal should outline, as specifically as possible, the technical details of the proposed research. In addition, proposals should articulate how research goals align with the research objectives outlined in the previous section. Proposers can enhance or deviate from these topics if they provide reasonable technical arguments and if they are still addressing the required objectives. Efforts integrating a variety of approaches are preferred to those using a single approach, especially as these may provide increased opportunities for information and technology transfer to AFRL.

4.2 Investigator Qualifications

In line with the basic research vision of this COE, proposals should highlight the following qualifications of the proposed academic collaborators:

1. Strong history of published research that is both principled and foundational in the fields of brain-inspired dynamic nanomaterials, materials for extreme environments, non-linear computing circuits, and neuromorphic computing circuits and systems.

2. Expertise within the academic team covering the range of computer science, materials science, and electrical engineering with potential contributions from mathematics and/or physics.

3. Strong history of applying principled neuromorphic models, self-learning algorithms, dynamic data analytics, and/or hardware systems for learning to real-world problem settings.

4. Willingness to collaborate on and commit resources to jointly defined projects with AFRL scientists and engineers (S&Es).

4.3 Interactions and Information Exchange

Proposals should address plans for the following interactions between AFRL S&Es and academic collaborators:

1. One or more research projects, jointly defined by the academic collaborators and S&Es at AFRL that are in line with collaborators’ expertise, AFRL interest, and the goals of this proposed COE.

2. Commitment from academic collaborators of graduate students and/or post-doctorate associates to work on the aforementioned project(s) both at the academic institutions and embedded with S&Es at AFRL, working on USAF and USSF data and learning tasks.

3. A yearly workshop, independent of annual meetings, where topics are jointly defined by the academic collaborators and AFRL S&Es. Such a workshop would be organized by the academic collaborators, include invited speakers, and be located at an AF/DoD facility.

4. If applicable, how the COE will leverage other institutional resources to expand the participation of students and postdoctoral fellows in the COE, establish dedicated facility and office space, and/or other means of promoting research.

Proposers are encouraged to confer with the designated points of contact as soon as possible.

Their contact information can be found at the end of this announcement. Coordination with AFOSR, AFRL/RI, and AFRL/RX prior to proposal submission is encouraged but is not required.

4.4 Access to DoD Resources

Proposals may request access to AFRL facilities or DOD high performance computing resources in order to conduct the proposed research. Proposals should make this request in accordance with the instructions given in the D.4.g. Project Narrative section of this announcement. If authorized, there is no cost to the research for these resources.

Applicants are advised that routine access of educational institution researchers to AFRL/RI and AFRL/RX buildings and facilities is limited to U.S. citizens. Individuals eligible for access are subject to background checks. Section C.3.a. Research Personnel Facility Access Requirements and Restrictions provides more information.

Award(s) under this FOA are not restricted to U.S. citizens, but access to DoD facilities is limited for non-US citizens, which could add coordination challenges. An objective of this COE is to establish relationships between researchers at the performing universities and the relevant AFRL Directorates. If relevant, proposals should include information on U.S. and non- U.S. personnel, describing their roles in the research effort.

B. FEDERAL AWARD INFORMATION

AFOSR anticipates making one award under this announcement, through an issued grant. Any award made under this competition will support a University Center of Excellence (COE) in “Extreme Neuromorphic Materials and Computing,” and is subject to availability of funds.

AFOSR executes discretionary research and development funds appropriated to the USAF and the USSF for awards. AFOSR can only make an award if sufficient funds are available.

The anticipated period of performance is a three-year base period, with one two-year option to continue performance. As a result, the total period of performance if all options are exercised is five (5) years.

AFOSR anticipates making at least one grant award of up to $1,000,000 per year per award, for a maximum of five years. The base period is for three years, which the AFOSR intends to incrementally fund, followed by an option to extend an additional two years provided the COE passes a midterm “go/no-go” review at the 2.5-year point. All funding decisions are at the Government’s discretion and are subject to the availability of funds.

This plan means proposers should plan on not more than $5,000,000 in funding for the entire five-year duration if all options are exercised; however, the total amount of funding and resources made available to fund a successful proposal may vary based on the quality of proposals received, and funds availability.

AFOSR reserves the right to select and fund for award all, some, part, or none of the proposals received. There is no guarantee of an award.

Our authority for an award under this competition is established at 10 U.S.C. 2192(b)(1)(B) for improvement of education in technical fields, and 10 U.S.C. 4001 for basic and applied research.

We discuss regulations, terms, and conditions that generally apply to our awards in Section F.

Federal Award Administration Information.

KEY DATES

Schedule of Events

Event Date Eastern Time

Pre-proposal inquiries and questions regarding eligibility and technical requirements ** 29 June 2023 NLT 11:59 PM

Answers to questions posted 6 July 2023 White Papers Due (highly encouraged) 20 July 2023 NLT 11:59 PM

Notifications of Evaluations of White Papers 3 Aug 2023 Proposals due 28 Aug 2023 NLT 11:59 PM

*Notification of Selection for Award 8 Nov 2023

*This date is an estimate as of the date of this announcement.

**Questions submitted after the Q&A deadline may not be answered.

https://uscode.house.gov/view.xhtml?req=granuleid%3AUSC-prelim-title10-section2192&num=0&edition=prelim

C. ELIGIBILITY INFORMATION

1. ELIGIBLE APPLICANTS

a. Qualified and Responsible United States Educational Institutions You are eligible to submit an application if you are a qualified and responsible educational institution in the United States as defined at 10 U.S.C. 2194, or an entity comprised of such educational institutions. Educational institution means a local educational agency, college, university, or any other nonprofit institution dedicated to improving science, mathematics, and engineering education. No other entities are eligible to submit applications under this competition. Any entities receiving subawards must meet these same criteria. Since the intent of this FOA is to fund a research center with co-located researchers and facilities, at least 40 percent of key personnel (i.e., PIs and co-PIs) for a proposed COE must be primarily employed by the institution submitting the proposal.

AFOSR reviews your application, proposal, and Office of Management and Budget (OMB) designated repositories of government-wide public and non-public data, including comments you have made, as required by 41 U.S.C. 2313 and described in 2 CFR 200.205 and 32 CFR

22.410 to assess risk posed by applicants, and confirm applicants are qualified, responsible, and eligible to receive an award.

b. HBCU/MI, Tribal College and University Applicants Encouraged Historically Black Colleges and Universities and Minority Serving Institutions (HBCUs/MSIs) and Tribal Colleges and Universities are encouraged to submit research proposals and join others in submitting proposals. However, no funds under this announcement are reserved or otherwise set-aside for any specific entity type. The Air Force will only use the E.1. Criteria for proposal selection.

c. Ineligible Entities None of the following entity types are eligible to submit proposals as primary award recipients under this announcement:

(1) Federally Funded Research and Development Centers (FFRDCs)

(2) Individual persons or people

(3) Federal agencies (to include Military Educational Institutions)

(4) For-profit institutions

2. COST SHARING OR MATCHING

Cost sharing or matching is neither required nor an evaluation criterion for proposals under this announcement. Leveraging other institutional resources to increase the participation of students and postdoctoral fellows in the COE and/or promote research and relationships with AFRL to benefit the COE would enhance the collaboration plan that will be evaluated as part of the DoD relevance criterion for proposals under this announcement.

3. OTHER

a. Research Personnel Facility Access Requirements and Restrictions https://www.ecfr.gov/current/title-2/subtitle-A/chapter-II/part-200/subpart-C/section-200.205 https://www.ecfr.gov/current/title-32/subtitle-A/chapter-I/subchapter-C/part-22

A key aspect of this CoE is workforce development. It is envisioned that students and post-docs working under this CoE be able to collaborate with AFRL personnel at AFRL laboratories and facilities. As such, we require that any students and post-docs funded by this CoE be U.S. citizens or permanent residents. Additionally, AFRL contains facilities and equipment that could be useful to this Center. Access to these facilities is restricted to U.S. citizens or permanent residents.

b. Acknowledgment of Support and Disclaimer Requirements

You must include the F. 3.d. Acknowledgment of Research Support on all materials created or produced under our awards. The F. 3.e. Disclaimer Language must be included on materials as required. The award document may provide additional instructions about specific distribution statements to use when you provide research materials to us. You are not eligible to submit a proposal if you cannot accept these terms.

c. Expectation of Public Dissemination of Research Results

AFOSR expects public dissemination of research results if you receive an award. This is a basic requirement for unclassified research results.

AFOSR intends, to the fullest extent possible, to make available to the public all unclassified, unlimited peer-reviewed scholarly publications and digitally formatted scientific data arising from research and programs funded wholly or in part by the DoD as described in the OUSD, AT&L Memorandum, “Public Access to Department of Defense-Funded Research” dated 09 Jul 2014.

AFOSR follows DoD Instruction 5230.24 and DoD Instruction 5230.27 policies and procedures to ensure broad dissemination of unclassified research results to the public and within the Government. The DoD Instruction 5230.27 policy and procedures allowing publication and public presentation of unclassified fundamental research results will apply to all research proposed under this competition unless the Program Officer gives you an explicit, written exclusion to these policies with the Grants Officer’s advice and consent. All exclusions must be authorized or required by law and must cite a valid legal authority.

Your eligibility for funding cannot be determined unless this form is received.

https://discover.dtic.mil/public-access-requirements-incorporated-into-the-dod-stip/ https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/523024p.pdf?ver=JiZUVfNZrPKmcRMim_UnHg%3d%3d https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/523027p.pdf?ver=2019-04-04-095230-883 https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/523027p.pdf?ver=2019-04-04-095230-883

d. Conflict of Interest

This announcement requires that all current and pending research support, as defined by Section 223 of the FY21 National Defense Authorization Act must be disclosed at the time of proposal, for all covered individuals. Such disclosure will be updated annually during the performance of any research project selected for funding, and whenever covered individual are added or identified as performing under this project. Covered Individuals are those who are listed as key personnel on proposals including but not restricted to the principal investigator or co-principal investigator.

Any decision to accept a proposal for funding under this announcement will include full reliance on the applicant’s statements. Failure to report fully and completely all sources of project support and outside positions and affiliations may be considered a material statement within the meaning of the Federal False Claims Act, and constitute a violation of law.

The funding agency may conduct a pre-award conflict of interest/conflict of commitment review of any proposal selected for funding, as defined in National Science and Technology Council Guidance for Implementing National Security Presidential Memorandum 33 (NSPM-33) on National Security Strategy for United States Government-Supported Research and Development (January 2022), at p. 7 (available at https://www.whitehouse.gov/wp-content/uploads/2022/01/010422-NSPM-33- Implementation-Guidance.pdf). Offerors are advised that any significant conflict of interest/conflict of commitment identified may be a basis for the rejection of an otherwise awardable proposal.

Applicants for assistance are required to comply with 2 CFR 200.318(c), Codes of Conduct, to prevent real or apparent conflicts of interest in the award and administration of any contracts by which a recipient or subrecipient purchases property or services, supported by federal funds.

1) General Requirement for Disclosure

You and your organization must disclose any potential or actual scientific or non-scientific conflict of interest(s) to us. You must also disclose any potential or actual conflict(s) of interest for any sub recipient you include in your proposal. You must provide enough information for AFOSR to evaluate your disclosure. AFOSR may have to ask you more questions if more information is needed.

At AFOSR’s sole discretion, you may be asked for a conflict of interest mitigation plan after you submit your proposal. Your plan is subject to AFOSR approval.

2) Scientific Conflict of Interest

Scientific collaborations on research and development projects are generally the result of close collaboration prior to the submission of applications for support.

Accordingly, virtually all of these collaborations might be considered to include a https://www.whitehouse.gov/wp-content/uploads/2022/01/010422-NSPM-33-Implementation-Guidance.pdf https://www.whitehouse.gov/wp-content/uploads/2022/01/010422-NSPM-33-Implementation-Guidance.pdf potential conflict of interest. The potential conflict is mitigated by the disclosure of these collaborations, and the list of current and pending support you provide for senior and key researchers.

You must provide a copy of all peer-reviewed publications developed or produced from research conducted with Air Force funds to the AFOSR Program Officer.

D. APPLICATION AND SUBMISSION INFORMATION

1. ADDRESS TO REQUEST APPLICATION PACKAGE

All the application forms you need are available electronically on Grants.gov. From the View Grant Opportunity” page, you can click on the “Application Package” tab to download the application package.

You can find the electronic application package on Grants.gov by searching for the announcement number shown on page one. Paper copies of this announcement will not be issued.

Please contact us at afosr.baa@us.af.mil to request a reasonable accommodation for any accessibility requirements you may have.

2. CONTENT AND FORM OF APPLICATION SUBMISSION

a. Pre-proposal Inquiries and Questions You are encouraged to contact the Program Officer listed in section G.1. Technical Inquires and Questions before you submit your proposal.

If you need help with general matters, you should contact the individuals listed in G.2.

General Inquiries and Questions.

Your pre-proposal inquiries and questions should be submitted not later than 11:59 PM Eastern Time on 29 June 2023. AFOSR may not be able to answer questions received later. This is discussed more in section D.7. Submission Dates and Times.

The Program Officer does not have the authority to make commitments for the government.

Grants and Contracting Officers acting within their warranted capacity are the only people authorized to make commitments for the Government.

b. The Application as a Whole You must submit your proposal electronically through Grants.gov. AFOSR will not accept or evaluate any proposal submitted by any means other than through Grants.gov. AFOSR must receive your proposal before the D.7.d. Proposal Submission Deadline.

You must use the electronic Standard Form (SF) 424 Research and Related (R&R) Form Family, OMB Number 4040-0001. The SF 424 (R&R) Application for Federal assistance form must be your cover page. No pages may precede the SF 424 (R&R).

https://www.grants.gov/ http://www.grants.gov/ mailto:afosr.baa@us.af.mil

You must mark your application with the announcement number.

A summary of what is required for a complete proposal is summarized below:

The forms and attachments in bold text are required with all applications Some applications require the attachments in italic More instructions are provided in D.4. Component Pieces of the Application

R&R FORM, OMB No. 4040-0001 FIELD ATTACHMENT SF 424 (R&R) Application for Federal 18. Representation for Tax Assistance, including an authorized Delinquency, Felony Signature Conviction, and Internal

Confidentiality Agreements

18. SFLLL Disclosure of Lobbying Activities R&R Other Project Information Form 7. Project Summary /

Abstract

8. Project Narrative

9. Bibliography & References Cited

10. Facilities and Other Resources R&R Other Project Information Form 11. Equipment

12. Other Attachments R&R Senior / Key Person Profile Form Biographical Sketch

Current & Pending Support R&R Budget Form Budget Justification R&R Sub award Budget Attachments Form Sub award Budget

Justification R&R Project / Performance Site None Locations Form R&R Personal Data (Optional) None

The SF 424 (R&R) must include the…