DoD SBIR 23.3 BAA

Active
No
Status
Closed
Release Date
August 23rd, 2023
Open Date
September 20th, 2023
Due Date(s)
October 18th, 2023
Close Date
October 18th, 2023
Topic No.
AF233-D023

Topic

TAK Mobile Machine Learning (MML) Model Development

Agency

Department of DefenseN/A

Program

Type: SBIRPhase: BOTHYear: 2023

Summary

The Department of Defense (DoD) is seeking proposals for the topic "TAK Mobile Machine Learning (MML) Model Development" as part of the SBIR 23.3 BAA. The objective of this topic is to develop and train cutting-edge machine learning models for edge deployment via TAK (Tactical Assault Kit) using the Model Integration Software Toolkit (MISTK) format. The technology is restricted under ITAR or EAR regulations. The training can be done server-side, but inference must be done on the device. Offerors are provided with TAK-ML, a client and server-side framework for ML development, and NodeDrop, a technology to reduce the size of neural networks. Sample models/algorithms developed in and integrated with TAK-ML are provided. The potential applications of this technology include geolocation, command and control, search and rescue, surveillance, communications, IoT, cloud, and intelligence. The project will have a Phase II, which includes data collection, model design, implementation, training, testing, and evaluation at the tactical edge. Successful Phase II technology development may be eligible for additional Phase III work, with specific transition paths depending on the domain and problem set selected. The proposer will work with the Tactical Assault Kit (TAK) Product Center and end-user communities to promote the transition of machine learning models. The project is open for proposals until October 18, 2023. For more information, visit the DoD SBIR 23.3 BAA on grants.gov or the DoD SBIR/STTR website.

Description

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Integrated Network System-of-Systems

 

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

 

OBJECTIVE: The objective of this topic is to develop and train cutting-edge machine learning models for edge deployment via TAK using the Model Integration Software Toolkit (MISTK) format.

 

DESCRIPTION: Training can be accomplished server-side, but inference must be done on device. TAK-ML, a client and server-side framework for ML development, and NodeDrop, a technology to reduce the size of neural networks without affecting efficacy, are provided to performers. Sample models/algorithms developed in and integrated with TAK-ML are provided (e.g., biometrics, edible plants). Example use cases may include, but are not limited to geolocation, command and control, search and rescue, surveillance, communications, IOT, cloud or intelligence (including open-source intelligence). Use of digital engineering tools to at a minimum define the APIs and where applicable build reference implementations is preferred. Leveraging TAK-ML and StreamlinedML to integrate into the TAK ecosystem is strongly preferred.

 

PHASE I: This topic is intended for technology proven ready to move directly into a Phase II. Therefore, a Phase I award is not required. The offeror is required to provide detail and documentation in the Direct to Phase II proposal which demonstrates accomplishment of a “Phase I-like” effort, in this instance demonstrating familiarity and proficiency with applied machine learning, preferably at the tactical edge.

 

PHASE II: As an applied ML topic, Phase II objectives mirror standard machine learning lifecycle steps to include data collection, model architecting and design, implementation either standlone or via registration/integration with provided AFRL toolkits, training, testing, and evaluation at the tactical edge.

 

PHASE III DUAL USE APPLICATIONS: Successful Phase II technology development will be eligable for additional Phase III work, with specific transition paths depending on the domain and problem set selected by the proposer. AFRL will work with the Tactical Assault Kit (TAK) Product Center (TPC) and domain-relevant end-user communities to promote transition of machine learning models that reach sufficient TRL (5-7) and interface well with mobile end-user devices in use by operators in the field.

 

REFERENCES:

https://mistkml.github.io/;https://github.com/raytheonbbn/tak-ml;https://dl.acm.org/doi/abs/10.1109/MILCOM52596.2021.9652909;
https://tak.gov;https://civtak.org

 

KEYWORDS: mobile machine learning; end-user devices; edge computing; machine learning/artificial intelligence; resource constraints