United States

From biomedical sensing and monitoring to DVS camera output classification and RF fingerprinting, many applications require the ability to learn and adapt to spatiotemporal patterns, that is, patterns in multichannel time sequences. Spiking neural networks (SNNs) are bio-plausible computing models with high energy efficiency. The temporal dynamics of neurons and synapses enable them to detect temporal patterns and generate sequences. While Backpropagation Through Time (BPTT) is traditionally used to train SNNs, it is not suitable for online learning of embedded applications due to its high computation and memory cost as well as extended latency. In this work, we present Spatiotemporal Online Learning for Synaptic Adaptation (SOLSA), which is specifically designed for online learning of SNNs composed of Leaky Integrate and Fire (LIF) neurons with exponentially decayed synapses and soft reset. The algorithm not only learns the synaptic weight but also adapts the temporal filters associated to the synapses. Compared to the BPTT algorithm, SOLSA has much lower memory requirement and achieves a more balanced temporal workload distribution. Moreover, SOLSA incorporates enhancement techniques such as scheduled weight update, early stop training and adaptive synapse filter, which speed up the convergence and enhance the learning performance. When compared to other non-BPTT based SNN learning, SOLSA demonstrates an average learning accuracy improvement of 14.2%. Furthermore, compared to BPTT, SOLSA achieves a 5% higher average learning accuracy with a 72% reduction in memory cost. The extremely low computational cost of the learning algorithm allows us to perform both inference and training on the same FPGA platform, enabling true autonomy for mobile systems.

NAML 2026

Neuromorphic Spatiotemporal Online Learning for Synaptic Adaptation

temporal dynamics

spatiotemporal learning

spiking neural networks

time series classification

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



technical paper

### Welcome to the 10th annual workshop on Naval Applications of Machine Learning!

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The 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



Naval operations increasingly depend on artificial intelligence (AI) models that process massive multi-modal sensor data to enable real-time perception, classification, and autonomous decision-making across ships, submarines, distributed sensors, and unmanned surface or underwater vehicles. These platforms operate in communication-denied, power-constrained, and harsh environments where cloud offloading and GPUs maybe impractical. Many naval assets, especially those at high altitude, in space-adjacent environments or near nuclear propulsion, also face elevated radiation exposure, making radiation-tolerant hardware essential. Conventional digital technologies incur significant overhead to mitigate radiation-induced faults, whereas emerging nonvolatile memories such as Resistive Random Access Memories (ReRAM) offer inherently higher radiation tolerance. In-memory computing (IMC) architectures further reduce energy and latency by eliminating the von Neumann data-movement bottleneck.
To meet these demands, we propose Waikiki, a ReRAM-based, radiation-tolerant edge AI accelerator designed for fast and efficient inference of deep neural networks (DNNs). Existing IMC accelerators struggle with modern AI models that require both linear layers and scaled dot-product attention, which rely on data-dependent matrix multiplications and complex nonlinear functions. They also require large, power-hungry analog-to-digital converters (ADCs) and spend most of their energy on analog-digital data processing. Further, they rely on external digital vector functional units for complex activations, limiting performance and suffer from low utilization when mapping full models to finite-size analog cores. Waikiki overcomes these limitations through the NL-DPE core, a Non-Linear Dot Product Engine that integrates conventional crosspoint arrays with ReRAM-based Analog Content Addressable Memories. This enables arbitrary nonlinear functions and data-dependent matrix multiplies to be executed directly in the analog domain as decision trees, while eliminating ADCs and enabling scalable radiation-robust analog computation. Noise resilience is further improved through software-based Noise Aware Fine-Tuning, avoiding costly in-device calibration and mitigating analog non-idealities.
We evaluate NL-DPE on diverse AI workloads, including CIFAR-10 and ImageNet, which are representative of embedded vision and onboard data-analysis tasks in naval environments. NL-DPE delivers 28× energy efficiency and 249× speedup over a GPU baseline at equal quantization, and 22× energy efficiency with 245× speedup compared to existing IMC accelerators, while maintaining high computational accuracy. Within Waikiki’s compact 64-core architecture, we demonstrate efficient model compression and mapping via singular value decomposition. The system achieves ResNet-50 inference at 7.5× compression, maintains >92% CIFAR-10 accuracy, and provides >3000× energy-efficiency improvement over the digital baseline.

Waikiki, an analog in-memory AI accelerator for edge environments

Hierarchical Spiking Density: An Event-Based Implementation of HDBSCAN using Neuromorphic Framework

Abstract
The rapid growth of edge-based sensors requires unsupervised learning algorithms capable of processing asynchronous, high-velocity data streams under strict power constraints. This is particularly critical for Event-Based Sensor (EBS) object tracking applications, where immediate, localized cluster analysis is necessary to identify and track objects from high-speed, noisy sensor data. While density-based clustering, specifically Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is a standard for identifying clusters of arbitrary shapes, it struggles with data containing varying density gradients and requires sensitive hyperparameter tuning. The hierarchical extension, H-DBSCAN, mitigates these issues by constructing a hierarchy of cluster stability, but its traditional recursive implementation is computationally prohibitive for real-time, resource-constrained neuromorphic hardware.
This research proposes a novel Spiking Neural Network (SNN) architecture that approximates the HDBSCAN algorithm, simulated within a specific Neuromorphic Computing Framework. This SNN-based H-DBSCAN is designed to efficiently perform the clustering required for EBS object tracking. By triggering a wave-front propagation of spikes, the network inherently computes a Minimum Spanning Tree (MST) equivalent through a "first-to-fire" mechanism. A multi-tiered topology integrates spikes over varying temporal windows, effectively constructing a tree diagram of cluster persistence to extract stable clusters without prior knowledge of the data's scale.
The proposed architecture will be validated through edge and hardware-in-the-loop implementation targeting traditional CUDA based devices and by FPGAs. The SNN design will be synthesized using a known framework neuro-processor backend, configured for a small neuron array with sparse connectivity. Results will include the comparison between the SNN and CUDA based implementation of the network based on accuracy, speed, and power consumption of each platform.



Hierarchical Spiking Density: An Event-Based Implementation of HDBSCAN using Neuromorphic Framework

Spectrum AI Agent for Monitoring a Radio Dynamic Zone
Silvia Ionescu (sionescu@mitre.org)
Curtis Watson (cmwatson@mitre.org)

The increasing reliance on wireless communications highlights the critical need for innovative spectrum sharing strategies to maximize the utilization of finite resources. Dynamic spectrum environments require continuous monitoring and agile decision-making, yet the volume and complexity of radio frequency (RF) data can overwhelm human operator and hinder timely, accurate responses. There is a pressing need for tools that reduce cognitive overload while enabling robust, real-time spectrum coexistence in diverse, evolving operational contexts. 
The NSF National Radio Dynamic Zones (NRDZ) Spectrum Applications with Real-world Knowledge through Integrated Experiments (SPARKIE) project advances spectrum management through long-term dynamic spectrum sharing experimentation and the integration of cutting-edge artificial intelligence (AI) technologies. SPARKIE introduces a spectrum-aware AI agent that leveraged machine learning (ML) to continuously analyze RF data for dynamic, efficient monitoring and decision-making. By deploying sensing capabilities at edge sensor nodes equipped with high-performance GPUs, such as DeepWave AIR-T devices that combine software defined radio functionality with GPU acceleration, the project strives to enable real-time sensing, monitoring, and decision support. These edge-based tools are further augmented with large language model (LLM)-based agents that provide contextual reasoning and decision-making, spectrum monitoring, and evaluation of experimental outcomes. 
In this presentation, we will present results from the NRDZ AI spectrum agent’s evaluation of the November 2025 Hat Creek Radio Observatory spectrum coexistence experiments. Our results highlight the AI agent’s ability to assess experiment outcomes and deliver refinement suggestions that accelerate and improve decision-making. 

Approved for Public Release; Distribution Unlimited Public Release Case Number 25-02739-2. © 2025 The MITRE Corporation. ALL RIGHTS RESERVED


Spectrum AI Agent for Monitoring a Radio Dynamic Zone

Naval radar systems operating near the 3.55–3.7 GHz Citizens Broadband Radio Service (CBRS) band face an increasingly complex RF environment, sharing spectrum with dense commercial networks and non-cooperative emissions. In coastal and port regions, these sources introduce clutter-like artifacts and reduce receiver sensitivity, particularly when atmospheric ducting extends the reach of land-based LTE/5G networks. Because this interference can erode radar performance long before operators notice visible degradation, there is a critical need for methods that not only detect interference but also quantify its impact on mission capabilities.

To address this, we present a two-stage machine learning framework designed to enhance both situational awareness and detection resilience in congested spectrum. First, we introduce RFI-Net, a physics-guided U-Net architecture that analyzes time–frequency data to characterize the spectral environment. Unlike simple energy detectors, RFI-Net learns the distinct signatures of radar waveforms, 5G OFDM blocks, narrowband tones, and impulsive bursts. A custom physics-guided loss function enables the model to separate interference from the underlying noise floor, providing operators with a real-time spectral occupancy map. This allows for the direct quantification of spectral interference map, enabling proactive adaptation of radar parameters if needed. 

Secondly, we developed a Convolutional Recurrent Neural Network (CRNN) detector capable of identifying radar pulses in extreme low-SINR regimes that can be used for CBRS Environmental Sensing Capability (ESC) sensors. While current FCC regulations for CBRS-ESC sensors require detection down to approximately -89 dBm/MHz our CRNN model achieves >99% detection probability at -10 dB SINR. This is an improvement of nearly 30 dB over standard regulatory baselines. By leveraging both local spectral features and temporal dependencies, the model maintains high recall even when radar pulses are submerged beneath structured OFDM signals or bursty wideband noise. 

We validated this framework on a comprehensive dataset comprising NTIA-specified radar waveforms mixed with realistic LTE/5G traffic and heterogeneous interference sources (tones, harmonics, and nonstationary bursts). The resulting system is highly efficient: the detector operates on single spectrogram snapshots with sub-4 millisecond inference latency. While the proposed approaches offer a radar detection capability for coastal monitoring and ESC applications, similar architectures provide detection of 5G waveform detection at naval radar receivers as well that can potentially provide enhanced RFI mitigation and situational awareness in shared spectrum environments.


Deep Learning for Resilient Spectrum Sharing: Physics-Guided Interference Mapping and Low-SINR Radar Detection in the CBRS Band

AI-Enhanced Undersea Signal Processing for Long-Range Vessel Classification

The ability to detect and classify surface vessels at long ranges—beyond visual and radar limits—is critical for distributed maritime operations and decision superiority. Acoustic sensing platforms offer unique advantages for this mission, yet classification remains challenging due to highly non-stationary signals, pervasive environmental clutter, and limited labeled datasets. The Office of Naval Research (ONR) has identified this capability as a research priority. Avathon AI proposes an innovative approach that fuses physics-based modeling with advanced machine learning to deliver robust performance in complex acoustic environments.
Our framework integrates multiple technical components:
• Physics + AI Hybrid Models to improve acoustic imaging and classification beyond traditional statistical methods.
• Mass Feature Construction (MFC) across MFCC, spectral, wavelet, and time-domain features to identify predictive patterns at scale.
• Transformer-Based Architectures, including Masked Spectrogram Modeling (MSM-MAE), for unsupervised labeling and resilient embeddings in cluttered environments.
• Data Augmentation leveraging large public acoustic datasets to bootstrap models for naval-specific signatures.
This approach enables rapid adaptation to new vessel classes with minimal labeled data, improves classification accuracy under distortion and interference, and supports operational decision-making at the tactical edge. During the proposed 20-minute session, Avathon will demonstrate feature construction workflows, transformer-based classification on representative datasets, and discuss transition pathways for ONR and NIWC Pacific programs focused on ISR, undersea warfare, and multi-domain awareness.
Avathon AI brings over a decade of experience delivering AI/ML solutions for government and industry, including recent acoustic projects with NSWC. Our proven track record in predictive maintenance, ISR fusion, and autonomy platforms positions us as a strong partner for advancing naval AI capabilities.

AI-Enhanced Undersea Signal Processing for Long-Range Vessel Classification

By implementing an engineering-grade Naval GenAI framework constructed from extensive historical fleet and vessel maintenance datasets—including structured maintenance logs, free-text technician notes, condition-based monitoring outputs, and other unstructured data sources—naval personnel can leverage advanced machine learning and natural language processing pipelines to accurately determine inspection requirements, fault signatures, and component-level repair procedures. The system integrates supervised learning models, anomaly-detection algorithms, and transformer-based language models to correlate past failure modes with current system states, enabling the automated identification of probable root causes and the precise parts, tools, and technical steps required for corrective maintenance.
The GenAI models generate near-real-time, data-driven diagnostic assessments that replicate and enhance traditional engineering analyses by producing recommended inspection sequences, stepwise repair instructions, and decision-support evaluations for component replacement, refurbishment, or overhaul. These recommendations are optimized using reliability-centered maintenance (RCM) principles, failure mode and effects analysis (FMEA) data, and historical mean-time-to-repair (MTTR) and mean-time-between-failure (MTBF) patterns to ensure engineering validity and operational relevance.
Developing a Navy-wide GenAI system for fleet and vessel maintenance offers engineering advantages far beyond conventional schedule-based preventive maintenance models. Instead of relying exclusively on fixed intervals or human judgment, the framework enables condition-informed, data-adaptive maintenance scheduling that reduces unnecessary work, minimizes downtime, and increases operational availability. The integration of historical work packages, parts-usage records, and prior diagnostic outcomes allows sailors, maintainers, and technical support engineers to derive actionable insights from decades of maintenance activity. This supports faster troubleshooting, automated diagnostic triage, higher accuracy in fault localization, and more consistent adherence to technical standards and repair specifications.
Ultimately, the engineering-focused GenAI framework enhances fleet sustainment by enabling predictive maintenance workflows, improving technical decision-making, and providing maintainers with reliable, AI-augmented guidance to optimize repair processes and lifecycle performance across naval platforms.


From Data to Diagnostics: How GenAI Revolutionizes Naval Maintenance

Purpose
The Navy’s introduction of Unmanned Surface Vessels (USVs) into distributed maritime operations requires new approaches to maintenance, readiness, and lifecycle management. Sustainment models designed for crewed platforms depend on manual inspection and reactive maintenance actions, which are not suitable for autonomous or semi-autonomous systems expected to operate persistently, at distance, and in degraded communications environments. This effort explores the application of artificial intelligence (AI) and data-driven methods to enable predictive, condition-based, and adaptive sustainment for the PMS 420 sUSV fleet.
Methodology
The architecture is designed to support integration of multiple data sources, including sensor telemetry, maintenance and configuration records, and operational context, within a unified data environment. 
A key capability currently demonstrated is the use of a generative AI (GenAI) interface that enables users to interact with the system in natural language rather than traditional query syntax. This interface leverages retrieval-augmented generation (RAG) and adaptive agent workflows to allow maintainers, engineers, and analysts to obtain contextual information, system insights, and maintenance recommendations through conversational interaction.
The architecture is designed for secure operation within DoD environments and aligns with DISA STIG, NIST SP 800-53, and IL4/IL5 compliance requirements. Model performance, data provenance, and decision traceability are continuously monitored. The framework supports future integration with Navy logistics systems and Digital Twin environments to enable predictive simulation and fleet-wide readiness assessments.
Results
Initial demonstrations focus on the GenAI interface and its ability to simplify human–machine interaction for maintenance and sustainment tasks. The system has shown that natural-language engagement can reduce diagnostic and troubleshooting time by making technical data more accessible to non-specialist users. Future work will focus on connecting live and historical data sources to further enhance predictive and condition-based capabilities.
Impact
This effort advances the concept of sustainment autonomy by establishing a scalable and secure framework that can evolve as data sources and operational requirements mature. The GenAI-driven interface represents a practical step toward enabling consistent, data-informed decision support across distributed maritime environments. The work contributes to PMS 420’s and the Navy’s broader objective of maintaining readiness through data-centric processes, intelligent automation, and continuous learning systems. By leveraging AI to augment, not replace, human expertise, the approach enhances the speed, transparency, and resilience of sustainment operations in support of future distributed fleet concepts.


From Data to Decision: An AI-Powered Sustainment Platform Enabling Predictive Readiness Across the USV Fleet

Modern destroyers generate far more engineering data than humans can interpret in real time. Legacy maintenance models using planned and casualty maintenance supported by tools like ICAS (Integrated Condition Assessment System) struggle to recognize anomalies in the data streams or turn this deluge of data into actionable recommendations that integrate into sailor workflows.

This talk introduces ERMv4, the Navy's Enterprise Remote Monitoring system which turns roughly 7,000 hull, mechanical, and electrical sensor readings per second into recommendations that feed the ship’s existing maintenance planning system. This shift has moved condition-based maintenance (CBM) from concept to operational tool on USS Fitzgerald (DDG-62).

We focus on three themes that connect ML research with naval requirements and fielded impact: 

ML at the tactical edge. 

ERMv4 enables the MLOps data flow for models to learn from noisy, heterogeneous sensor data across pumps, motors, power distribution, and auxiliary systems. Delivering useful signals from tactical platforms demands time-series preprocessing, anomaly and degradation modeling when failure labels are rare, and handling non-stationary behavior after maintenance or configuration changes. We also cover edge-compute realities: packaging models for afloat deployment, operating under SWaP and bandwidth limits, handling DDIL connectivity, and safely updating models on ships underway for months within policy and accreditation constraints. 

Human-in-the-loop maintenance. 

ERMv4 is built so the computer does not “boss” sailors around. Recommendations are ranked, explained, and integrated into existing maintenance systems in ways that leverage sailor expertise and reduce alert fatigue. UI and workflow choices make it clear why a recommendation is being made, how confident the system is, and what evidence backs it up. We share early lessons on trust: what ship’s force accepts, what they push back on, and how the system must evolve so sailors feel supported, not treated like warehouse workers executing AI-generated task lists. 

Fleet-level implications. 

ERMv4 is one of the first AI programs-of-record continuously monitoring ship health and offers a template for scaling CBM across classes and platforms. We discuss how consistent data models, shared anomaly patterns, and common integration points with Navy logistics and maintenance systems can support Distributed Maritime Operations and higher availability, and how “assured autonomy” for CBM—demonstrating model behavior, reliability, and limits—can build durable trust in recommendations. 

Attendees will leave with a grounded case study of naval ML running on the deck plates, plus practical lessons for building and deploying ML systems that sailors actually use in the fight.

Machine Learning at Sea with Speed: ERMv4 and Condition-Based Maintenance for Mission-Ready Destroyers

The threat of quantum computers breaking widely used public-key cryptography like RSA and ECC grows steadily as the machines mature. Fortunately, NIST has standardized a set of cryptographic algorithms known as Post-Quantum Cryptography (PQC) that are expected to be secure against both classical and quantum attacks. Migrating to PQC as soon as possible is critical for the Navy for several reasons:
1. Long-term security needs: The Navy's communications are often confidential and frequently require decades-long security. Communication integrity is essential for fleet operations and mission success. Post-quantum cryptography will protect this data against future quantum computing threats.
2. Harvest Now, Decrypt Later (HNDL): Nation-states are collecting encrypted communications now in anticipation of the day when a future quantum computer will be able to decrypt them. Regardless of when this occurs, the HNDL strategy provides urgency for the Navy to move away from vulnerable cryptography soon.
3. Infrastructure longevity: Naval systems and vessels have long operational lifespans (20+ years). Implementing quantum-resistant cryptography now will protect these long-lived assets against future threats.
4. Supply chain security: The Navy relies on complex supply chains that must be secured against potential quantum computing attacks.

However, there are multiple challenges in updating the Navy’s computing and communications systems. First, all vulnerable cryptography needs to be inventoried, whether it exists across the wire or on a network endpoint. Second, these unsafe algorithms need to be replaced with PQC while maintaining some minimum level of network performance. Finally, the migration should also build crypto-agility into the system so that future cryptographic transitions can be performed efficiently.

Noblis is addressing these challenges through our internal research. We have met with several vendors that offer cryptographic inventorying tools and have down-selected to the three best tools. We are also building a virtual network testbed that allows us to test and evaluate these tools to select the best one for our clients. We will also evaluate the new PQC algorithms on this testbed to understand and mitigate the effects of longer key lengths on network performance. Finally, we will use the testbed to develop strategies for improving crypto-agility.

We will be exploring the use of AI/ML during each step of internal research process, including for creating a representative set of networks for quickly evaluating inventorying tools and for identifying locations in a large network where vulnerable cryptography or high priority datasets are most likely to be present. By transitioning to PQC in a timely manner, the Navy can maintain communications security and operational effectiveness in an era when quantum computing will ultimately render current encryption methods unsafe.


Migrating to Post-Quantum Cryptography

Traditional analyses of high-speed craft (HSC) hydrodynamics depend on experimental, semi-empirical, and numerical approaches that can be computationally expensive and poorly suited for applications. This work presents a Neural Ordinary Differential Equation (Neural ODE) framework for learning the continuous-time dynamics directly from experimental data. Rather than discretizing the equations of motion, the model infers a smooth latent flow that maps sensor inputs to the vessel’s dynamic response. Trained with a noise-injection scheme to enhance robustness under perturbations, the Neural ODE achieves high accuracy in one-step-ahead prediction across calm-water and regular-wave conditions.
Additionally, this work will be extended and compared with discrete-time sequence models such as (TCN, LSTM). These results highlight the potential of continuous-time neural models for real-time hydrodynamic simulation and autonomous-vessel prediction within the broader context of computing with physical systems.

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