Field-Friendly Artificial Intelligence and Machine Learning Systems

STTR · Phase: BOTH · Topic DON27TZ02-NV002 · Solicitation 27.TZ

Current DNN architectures suffer from inherent "Black Box" vulnerabilities, demanding massive computational scaling and offering no clear link between parameter count and performance. The proposed model must address these shortfalls: - Optimized Resource Utilization: Unlike DNNs that heuristically overparameterize to boost accuracy, the proposed model must quantitatively map parameter count to expected performance and enable users to identify the minimum configuration required for mission success, significantly reducing computational overhead, complexity, and deployment costs. - Transparency: The model must replace opaque, distributed neural layers with novel methods for interpretability and trust that go beyond the current post-hoc techniques. By making input-output logic similar to standard programming code, the model should ensure decisions are easily explainable, verifiable, and compliant with auditing standards. - Reliability: The model must recognize whether it can provide a reliable output for a given problem instance, which will address another well-known criticism of DNNs wherein performance is typically measured as an average, e.g., average accuracy, over many instances of a problem and will help build user trust and acceptability of the model vs DNNs, particularly in sensitive areas like person identification in a crowd video and identifying the source of a cyber-security violation. The model must deliver DNN-level performance with superior speed and a reduced memory footprint, optimized for lean, secure processing on low-cost CPU and embedded edge hardware. By eliminating cloud-dependence, the model will maximize data privacy and operational security while substantially reducing power consumption compared to GPU-reliant models. It must operate flexibly across mixed CPU-GPU environments, allowing operators to dynamically balance throughput against computational cost. Beyond standard convolutional neural networks (CNNs), the model’s architecture must be extensible to transformer-based and multimodal models handling text, image, and video. The metrics below establish outer-edge targets specifically for CNN-based image classification. While satisfying every metric is not mandatory, the model will be evaluated on the number of targets achieved and the viability of the engineering trade-offs. 1. Comparison with Base Architecture of ResNet-18 and larger 2. Comparison of the Number of Classes 10 and 100 using 3 datasets: MNIST, CIFAR-10 and CIFAR-100, having different combinations of numbers of classes and total number of image pixels (number of channels*number of pixels per image) 3. Mean Average Accuracy of Classification: Same as DNNs or higher 4. Number of Parameters (Fraction of Base Architecture): 50% of DNNs or less 5. Training MFLOPs/Image (Fraction of Base Architecture): 50% of DNNs or less 6. Training Memory Size (Fraction of Base Architecture): 50% of DNNs or less 7. Inference MFLOPs/Image: 50% of DNNs or less 8. Rate of Growth of Metrics (5), (6), and (7): Sublinear In Phase II, similar metrics and their extensibility and relevance to transformers and multimodality scenarios (images and videos as well as their combinations with text to form LLMs and VLMs) will need to be provided and their implementation explained. This STTR topic requires combinations of research and development, and proposers must demonstrate a strong partnership between the small business and a research institution. The proposed solution should eliminate the dependency on complex computing infrastructure (e.g., GPUs) by delivering comparable performance on standard off-the-shelf hardware (CPUs, edge devices). It should optimize the cost-to-performance ratio, balancing accuracy with reductions in time, storage, and computational cost. Furthermore, the architecture should ensure robust explainability—like conventional software programs—and provide reliable confidence metrics to establish user trust. At a minimum, the method should result in t…

SBIR/STTR 은 미국 중소기업만 지원할 수 있습니다(Small Business Act 법정 요건). • 계열사를 포함해 상시 종업원 500명 이하 • 미국 시민 또는 영주권자 1인 이상이 50%를 초과해 직접 소유·지배 • 미국 내 사업장을 두고 주로 미국 내에서 사업을 영위할 것 • 수행책임자(PI)의 주된 근무처가 신청 기업일 것 출처: https://www.sbir.gov/faq/eligibility-requirements

Primary country

🇺🇸 United States

Also eligible

None

Foreign companies

No

Local entity

Required

Location condition

미국 내 사업장을 두고 미국 시민·영주권자가 50%를 초과해 소유한 중소기업만 신청할 수 있습니다(법정 요건).

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Official URL

https://www.dodsbirsttr.mil/topics-app/

Application URL

https://www.dodsbirsttr.mil/topics-app/

Last fetched

2026.11.06

Human review

Not reviewed — check the official announcement

First seen

2026.11.06

Data status

Auto-collected, not reviewed

AI enrichment

Needs verification