Integrating Neural Architectures for Brain-Inspired AI with Low SWAP

# Q&A Changes Summary **New Questions Added:** - Q1 (Updated): Clarifies that the listed architectures (CNNs, RNNs, transformers, SNNs) are illustrative, not prescriptive. Confirms that building on existing prototypes is acceptable and that functional emulation of neocortical properties is prioritized over biological accuracy. - Q2 (Updated): Reiterates that Phase I deliverables focus on four core goals, but proposers have flexibility in methodology to achieve them, including "alternative ways." - Q3 (Updated): Refocuses scope: while neuromorphic sensors are of interest, the primary emphasis is on the integrated multi-neural network architecture itself, not sensor-level hardware development. **Key Clarifications:** - Architecture flexibility emphasized (no 1:1 mapping required to biological structures) - Prototyping approach acceptable if aligned with research objectives - Functionality over biological realism is the priority - Sensors are secondary; core focus remains on unified AI architecture modeling neocortical processing No budget, timeline, or eligibility changes noted.

# Q&A Changes Summary **New Question Added:** - Q1 addresses whether Phase I proposals must strictly comply with all listed requirements or if alternate approaches "in the spirit" of requirements are acceptable if they meet subsequent criteria. **No substantive answer changes.** All previous answers (Q2-Q5) remain identical, covering: - Neuromorphic sensors and event-based camera feasibility - Low-SWaP evaluation methods and hardware targeting - Architectural progress metrics and Pareto comparisons - Dynamic resource allocation characterization - Abstract vs. sensory-based state representations - Data provision and neocortex-inspired design principles The update provides flexibility on methodology compliance while maintaining all technical and evaluation requirements.

# Q&A Changes Summary **New Questions Added:** - Q1: Two new questions about neuromorphic sensors and event-based camera processing scope—clarifying whether sensor-level hardware work (CMOS/ROIC circuitry, hardware actuation) is within topic scope. **Key Clarification:** The government added explicit guidance on neuromorphic sensor integration, indicating interest in sensor-processing work alongside the integrated architecture, though the response to Q1 appears incomplete in the provided update. **Note:** Questions 2-4 and their answers appear duplicated from the previous version with no substantive updates.

**Q1 received a new detailed answer** clarifying Phase I feasibility requirements: both simulation/small-scale prototypes AND low-SWaP hardware solutions required; no government-furnished data provided; both performance improvements and resource utilization metrics are valid; abstract state representations acceptable. Q2 and Q3 answers remained unchanged. Q4 appears incomplete/truncated in both versions.

# Summary of Q&A Changes **Added:** One new comprehensive Q&A (Q1) with 5 follow-up questions clarifying Phase I evaluation methodology: - Software profiling vs. hardware execution for low-SWaP feasibility - Hardware baseline/resource envelope specifications - Performance-resource tradeoff metrics and Pareto comparisons - Peak vs. average resource utilization characterization - Abstract vs. sensory input representation requirements - Government-furnished datasets availability **Renumbered:** Previous Q2, Q3, Q4 → Q2, Q3, Q4 (no content changes to existing answers)

No changes detected. The PREVIOUS and UPDATED Q&A sections are identical—all three questions and answers remain unchanged.

Added new answer to Q1 clarifying that both working memory/adaptive routing AND online parameter modification are relevant; proposers should highlight challenges and present metrics/benchmarks. Q2 and Q3 answers remain unchanged, emphasizing neocortex-inspired functional principles over specific mission optimization, and reiterating no datasets/OT constraints provided.

# Q&A Changes Summary Added 2 substantive new Q&As: **Q1 (new):** Clarifies adaptation requirements—asks whether adaptation should be a functional property (working memory, routing) or require online parameter modification in Phase I. **Q2 (new, 5-part):** Addresses architectural approach, biological inspiration level, component reusability/modularity, multi-paradigm instantiation requirements, and dynamic resource allocation mechanisms. **Q3 (repeat):** Original Lateos Inc. question re-posted with identical answer, emphasizing software-first approach, proposer-sourced datasets, and adherence to topic description deliverables.

Q1 received an answer clarifying: (1) No specific OT protocols/hardware mandated; software prioritized but must evidence valid hardware solutions for Low SWaP; (2) No datasets provided—proposer must demonstrate integrated neural architecture with their own compelling dataset; (3) Phase I deliverables defined in topic description.

This content appears to be an applicant's question to the program office rather than official guidance, and does not establish new requirements for prospective applicants. It merely documents one company's technical clarification requests about baseline parameters, reference data availability, and deliverable expectations for Phase I execution.

New opportunity: Integrating Neural Architectures for Brain-Inspired AI with Low SWAP