Part of the DoW SBIR/STTR FY27 Release 2 topic list. Browse all 83 topics by industry or service.
Topic DAF27BZ02-DV017 from the DoW SBIR 2027 BAA, Release 2. Pre-release now; proposals due January 6, 2027, 12:00 PM ET.
Quick answer
DAF27BZ02-DV017 is a Department of the Air Force Direct-to-Phase II SBIR topic seeking low-SWaP-C neuromorphic, event-driven sensing with onboard edge intelligence so an autonomous aircraft can remember an area, detect operationally meaningful change, and transmit what changed instead of raw imagery. No Phase I awards will be made. Proposals are due January 6, 2027, 12:00 PM ET.
Solicitation at a glance
- Topic number: DAF27BZ02-DV017
- Official title: Low - Cost Neuromorphic Edge Intelligence for Persistent Autonomous Change Detection and Machine Situational Memory
- Agency: Department of the Air Force
- Program: SBIR (DoW SBIR 2027 BAA, Release 2)
- Award type: Direct to Phase II (no Phase I awards)
- Award amount: Up to $2,000,000. Source: Department of the Air Force Release 2 instructions in DSIP.
- CMMC level: Level 2 (Self)
- ITAR: No
- Technology areas: Air Platform, Electronics, Sensors
- Pre-release (direct TPOC contact allowed): November 4 to December 2, 2026, 8:00 AM ET
- Opens in DSIP: December 2, 2026, 8:00 AM ET
- Proposals due: January 6, 2027, 12:00 PM ET
- Official source: DSIP Topics app (search DAF27BZ02-DV017)
BW&CO's take
What the Department of the Air Force wants
Airborne ISR wastes bandwidth, power, storage, and analyst time sending data when nothing has changed. The Air Force wants event-driven or neuromorphic sensors paired with onboard edge AI and persistent machine memory, so an autonomous aircraft compares current and past observations, rejects nuisance change from lighting, weather, vegetation, shadows, and motion, scores confidence, cues other sensors, and sends compact change products. Phase II covers six tasks: airborne sensor and edge architecture, change detection, situational memory, autonomous cueing, shared multi-UAS memory, and a flight demonstration. The end state is a TRL 6+ airborne prototype with measured reductions in bandwidth, processing, storage, power, or analyst burden.
Who is a good fit
- Existing event-based or neuromorphic vision hardware with measured power and SWaP data
- Change-detection algorithms with characterized false-alarm rates against environmental nuisance change
- Edge-processing or airborne demonstrations on small UAS, ideally at TRL 5 to 6
- Experience sharing compact data products across multiple aircraft through open interfaces to mission systems
Watch-outs
- This is Direct-to-Phase II: you must submit Feasibility Documentation showing Phase I-equivalent work completed outside SBIR funding, such as prototype sensors, airborne testing, or false-alarm characterization.
- Preferred entry maturity is TRL 5 to 6. Compelling TRL 4 technologies may be considered when the sensor and processing architecture has been demonstrated and can reach operational relevance within about three years.
- The Objective also references a neuromorphic IR sensor supporting celestial navigation for sUAS in GNSS-denied environments, while the rest of the topic centers on change detection. Consider asking the topic authors to clarify scope while direct contact is allowed (until December 2, 2026, 8:00 AM ET).
- The topic metadata lists CMMC Level 2 (Self). It is not marked as ITAR-restricted in the topic metadata.
Think your company fits DAF27BZ02-DV017? Talk with BW&CO before the January 6 deadline.
Plan Your DAF27BZ02-DV017 Submission
Official topic text (verbatim from DSIP)
The text in this section is copied word for word from the official DSIP topic as published on November 4, 2026. DSIP is the official source and topics can be amended. Always confirm the current version at dodsbirsttr.mil before you submit. Our commentary above is BW&CO's interpretation, not Government guidance.
Title
Low - Cost Neuromorphic Edge Intelligence for Persistent Autonomous Change Detection and Machine Situational Memory
Objective
Develop, integrate, and demonstrate a low-SWaP-C neuromorphic IR sensor to support celestial navigation to enable position and navigation of sUAS in a Global Navigation Satellite Systems (GNSS) denied environment in day and nighttime for world-wide operations. The desired system should allow an autonomous aircraft to establish a persistent machine representation of an area, recognize operationally meaningful changes, and communicate primarily what changed rather than continuously transmitting full-motion imagery or raw sensor data.
Description
Traditional airborne ISR architectures frequently collect and transmit large quantities of data even when little or nothing within the observed environment has changed. This creates substantial burdens on: Communications bandwidth. Processing. Storage. Power. Analysts. Command-and-control systems. Neuromorphic and event-driven sensors offer a fundamentally different sensing architecture in which the system responds primarily to changes within the observed environment.
The desired capability should combine event-driven sensing with onboard edge intelligence and temporal machine memory. Fundamental engineering considerations include: Event-driven/neuromorphic sensor characterization. Low-latency onboard processing. Low power consumption. Detection of operationally relevant changes. Rejection of irrelevant changes caused by lighting, weather, vegetation, shadows, sensor motion, or environmental clutter. Temporal comparison across repeated observations. Persistent representation of previous environmental state. Confidence scoring for detected changes. Autonomous prioritization of significant changes. Ability to cue additional onboard sensors. Generation of compact change products instead of continuous raw-data transmission. Multi-UAS sharing of relevant environmental changes. Open interfaces for integration with aircraft mission systems. Desired change classes may include movement, addition, removal, modification, displacement, construction, damage, altered activity patterns, or other Government-defined changes.
Phase I
As this is a Direct-to-Phase II (D2P2) topic, no Phase I awards will be made. To qualify, applicant(s) must provide Feasibility Documentation demonstrating prior completion of Phase I-equivalent efforts outside SBIR funding. Offerors should demonstrate feasibility through existing neuromorphic sensing, event-driven vision, edge AI, machine perception, autonomous change detection, or related capabilities.
Relevant evidence includes:
Prototype sensors. Hardware demonstrations. Airborne testing. Edge-processing demonstrations. Power/SWaP measurements. Change-detection performance. False-alarm characterization. Relevant commercial deployments.
Preferred maturity is TRL 5–6, although compelling TRL 4 technologies may be considered when the underlying sensor and processing architecture has been demonstrated and can reach operational relevance within approximately three years.
Phase II
Task 1 – Sensor and Edge Architecture Integrate event-driven sensing, processing, storage, autonomy, and aircraft interfaces into a representative airborne package.
Task 2 – Autonomous Change Detection Develop algorithms capable of distinguishing meaningful environmental change from irrelevant temporal variation.
Task 3 – Machine Situational Memory Develop persistent representations enabling the system to compare current observations with prior observations across meaningful time intervals.
Task 4 – Autonomous Cueing and Prioritization Allow detected changes to autonomously cue additional sensors or higher-fidelity processing and prioritize information for transmission.
Task 5 – Multi-UAS Situational Memory Demonstrate the ability for multiple aircraft to contribute to and consume a shared representation of detected environmental change.
Task 6 – Flight Demonstration Demonstrate the capability in an operationally relevant airborne environment.
Performance should quantify:
Probability of detecting relevant change. False-positive/false-change rate. Detection latency. Processing requirements. Power consumption. Bandwidth reduction. Data-storage reduction. Performance against environmental nuisance changes. Performance across repeated observations.
Desired Phase II End State
A TRL 6+ airborne prototype capable of autonomously identifying, prioritizing, storing, and communicating operationally relevant environmental changes while demonstrating measurable reductions in bandwidth, processing, storage, power, and/or analyst burden.
Phase III Dual Use Applications
Successful technology is intended to provide AFSOC, ACC, and operational organizations a rapidly fieldable sensing architecture capability for future electronic-warfare, communications, sensing, processing, and self-protection payloads. The transition owner is PAE ISR SOF / Asymmetrical Warfare Division – WISS. The architecture is intended to support future adaptation to additional platforms through further integration, development, and minimized cost burden. Follow-on funding is exploratory from AFSOC based on D2P2 results.
References
Bovolo, F., & Bruzzone, L. (2007). A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain. IEEE Transactions on Geoscience and Remote Sensing, 45(1), 218-236.
Chen, Y. H., Krishna, T., Emer, J. S., & Sze, V. (2017). Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE Journal of Solid-State Circuits, 52(1), 127-138.
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., & Scaramuzza, D. (2020). Event-based Vision: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 154-180.
Lichtsteiner, P., Posch, C., & Delbruck, T. (2008). A 128× 128 120 dB 15 µs Latency Asynchronous Temporal Contrast Vision Sensor. IEEE Journal of Solid-State Circuits, 43(2), 566-576.
Mead, C. (1990). Neuromorphic electronic systems. Proceedings of the IEEE, 78(10), 1629-1636.
Radke, R. J., Andra, S., Al-Kofahi, O., & Roysam, B. (2005). Image change detection algorithms: a systematic survey. IEEE Transactions on Image Processing, 14(3), 294-307.
Verhelst, M., & Moons, B. (2017). Energy-Efficient and Edge-AI processors for the IoT. 2017 IEEE International Electron Devices Meeting (IEDM).
Keywords
Neuromorphic Sensors, Event-Based Vision, Change Detection, Edge AI, Persistent ISR, Machine Memory, Event Cameras, Autonomous Sensing, Low-SWaP Computing, Multi-UAS Collaboration
Frequently asked questions
What is DAF27BZ02-DV017?
It is a Department of the Air Force SBIR topic for low-cost neuromorphic, event-driven sensing with onboard edge intelligence and machine situational memory, enabling an autonomous aircraft to detect, prioritize, and report operationally meaningful changes rather than streaming raw sensor data.
When is DAF27BZ02-DV017 due?
Proposals are due January 6, 2027, 12:00 PM ET. DSIP pre-release began November 4, 2026, and direct contact with the topic authors is allowed only until the topic opens on December 2, 2026, 8:00 AM ET.
Is this a Phase I or Direct-to-Phase II topic?
It is Direct-to-Phase II. No Phase I awards will be made, and applicants must provide Feasibility Documentation demonstrating Phase I-equivalent work completed outside SBIR funding.
What performance metrics will the Air Force look at?
Phase II performance should quantify probability of detecting relevant change, false-positive rate, detection latency, processing requirements, power consumption, bandwidth and data-storage reduction, performance against environmental nuisance changes, and performance across repeated observations.