Oscilon: A Modular Framework for Evolutionary Adaptive Intelligence in Resource-Constrained Environments
Modular AI architectures have emerged as a critical strategy for handling complex, multifaceted tasks by decomposing them into manageable, independent subcomponents. However, integrating Genetic Algorithms (GAs) into modular systems often intensifies computational demands, rendering them impractical for resource-constrained environments. This paper introduces Oscilon, a comprehensive modular framework designed to orchestrate Evolutionary Adaptive Intelligence (EAI) across task-specific subnetworks. Oscilon enables efficient deterministic AI on consumer-grade hardware, such as NVIDIA RTX 3090 GPUs or even experimental Raspberry Pi 3B+ clusters, by leveraging sparsity, parallelism, and dynamic resource allocation. We formalize Oscilon’s modularity through mathematical derivations of complexity bounds, demonstrating a scalable reduction in computational overhead to \(O(\max_m k_m \cdot D_m / P)\), where \(P\) represents the number of processors. Through extensive simulations on emergency department (ED) workflows in healthcare, Oscilon achieves up to 15x efficiency improvements over monolithic approaches, while maintaining deterministic outputs essential for high-stakes applications. Our framework not only addresses hardware limitations but also paves the way for adaptive, edge-deployable AI systems.
1. Introduction
The rapid advancement of artificial intelligence (AI) has led to increasingly complex tasks that demand flexible and scalable architectures. In domains like healthcare, where AI systems must manage interconnected subtasks—such as surgical risk mitigation, resource prediction, wait time estimation, and outcome forecasting—monolithic models often falter due to their inability to adapt efficiently to varying data distributions and computational constraints. Modular architectures offer a solution by partitioning the problem space into specialized submodules, each optimized for a specific aspect of the task. This decomposition not only enhances interpretability and maintainability but also facilitates parallel processing, crucial for real-time applications.
Neuroevolution, which evolves neural network topologies and weights through genetic algorithms (GAs), provides a powerful mechanism for optimizing these submodules. However, traditional neuroevolutionary methods, such as NEAT, operate on entire networks or large populations, incurring prohibitive computational costs that necessitate high-end hardware like NVIDIA A100 clusters. This limits their applicability in resource-constrained settings, such as edge devices in hospitals or low-power clusters.
To bridge this gap, we present Oscilon, a modular framework that integrates Evolutionary Adaptive Intelligence (EAI)—a targeted, scalar evolutionary approach—into a distributed, task-decomposed system. Oscilon’s core innovation lies in its ability to independently evolve subnetworks for each subtask, applying sparse EAI processes that focus on error-prone nodes while parallelizing computations across available hardware. This enables deployment on accessible platforms like RTX 3090 GPUs (with 24GB VRAM and 35.6 TFLOPS) or Raspberry Pi 3B+ clusters (despite challenges like communication overhead and limited RAM per node).
Our contributions are as follows:
1. A detailed architectural design for Oscilon, including modular decomposition, parallel orchestration, and integration with EAI.
2. Mathematical formulations for complexity analysis and optimization in resource-constrained environments.
3. Comprehensive empirical evaluations on healthcare benchmarks, including ablation studies and hardware profiling.
4. Discussion of practical implications, limitations, and future extensions for broader AI applications.
2. Related Work
2.1 Modular Neural Architectures
Modular neural networks have been explored since the early days of AI, with systems like Mixture of Experts (MoE) dynamically routing inputs to specialized “experts” based on gating mechanisms. In healthcare, modular designs have been applied to electronic health records (EHR) processing, where subtasks like diagnosis prediction and treatment recommendation are handled separately.
However, these approaches typically rely on gradient-based training, which introduces probabilistic elements and hallucinations. Oscilon differentiates itself by incorporating EAI for topology adaptation, ensuring deterministic evolution tailored to each module.
2.2 Neuroevolution Techniques
Neuroevolution algorithms evolve neural architectures by treating them as genomes subject to mutation, crossover, and selection. NEAT introduces innovation numbers to protect topological diversity, while HyperNEAT uses indirect encodings for larger-scale evolution. Modular extensions, such as Modular NEAT, allow for composable building blocks, but they still suffer from high evaluation costs due to population-wide fitness assessments.
Hybrid methods combine neuroevolution with gradient descent, like Evolved Transformer, but retain compute intensity. EAI addresses this by targeting individual nodes, and Oscilon builds on this by modularizing the process for parallel, task-specific optimization.
2.3 Distributed and Edge AI Frameworks
Frameworks like Ray and TensorFlow Federated support distributed training, but they are not natively designed for neuroevolution. Edge computing platforms, such as TinyML, focus on inference rather than training. Oscilon fills this void by enabling EAI training on heterogeneous hardware, drawing from distributed GA implementations while emphasizing sparsity for efficiency.
3. Methodology
Oscilon’s architecture is composed of three primary components: modular decomposition, parallel EAI orchestration, and dynamic resource management. We describe each in detail, supported by mathematical formulations.
3.1 Modular Decomposition
Given a complex task comprising \(M\) subtasks, Oscilon decomposes the overall neural network into \(M\) independent subnetworks. Each subnetwork \(m\) has its own weights \(W_m\), topology \(T_m\), and dataset subset \(D_m\), where \(W_m \ll W_{\text{total}}\) and \(D_m \subseteq D\).
Formally, the task output \( y = f(x; W, T)\) is reformulated as \(y = g(\{f_m(x_m; W_m, T_m)\}_{m=1}^M)\), where \(x_m\) is the input subset for subtask \(m\), and \(g\) is an aggregation function (e.g., ensemble voting or concatenation).
This decomposition reduces the search space per subnetwork, enabling targeted evolution.
3.2 Parallel EAI Orchestration
For each subnetwork m, Oscilon applies EAI [5] independently:
• Node Error Identification: Compute sensitivity \(s_{i,m} = |e_{i,m}| \cdot ||w_{i,m}||_2\) for nodes \(n_{i,m}\), selecting top \(k_m\) flagged nodes.
• Targeted Mutation: Mutate \(\Delta w_{i,m} = -\eta \cdot \nabla_{w_{i,m}} \text{Loss}(e_{i,m}) + \epsilon\), with fitness \(F(n_{i,m}) = \text{Acc}(n_{i,m}) - \lambda \cdot ||w_{i,m}||_0\).
• Thresholding: Iterate up to \(I\) times until \(F > \tau\).
The total complexity without parallelism is \(O(\sum_{m=1}^M k_m \cdot I \cdot D_m)\). With \(P\) processors (e.g., GPU cores or cluster nodes), Oscilon assigns subnetworks dynamically, yielding\( O(\max_m k_m \cdot I \cdot D_m / P)\).
For hardware-specific optimizations:
• RTX 3090s: Utilize CUDA for sparse matrix operations, batching mutations across flagged nodes.
• RPi 3B+ Clusters: Employ MPI for distribution, with load balancing to mitigate 8GB RAM limits per node. Communication overhead is modeled as \(O(M \cdot \log P)\), minimized by asynchronous updates.
3.3 Dynamic Resource Management
Oscilon includes a scheduler that monitors hardware utilization (e.g., VRAM, CPU) and adjusts \( k_m\), \(I\), and batch sizes. For instance, if VRAM exceeds 80%, reduce \(D_m\) or prune low-fitness nodes early.
Aggregation \(g\) handles inter-module dependencies via soft links (e.g., passing intermediate activations), evolved as part of the topology.
4. Experiments
4.1 Datasets and Setup
We evaluated Oscilon on two healthcare benchmarks:
• MIMIC-III ED Workflow: A real-world dataset with 450k+ ED visits, decomposed into 5 subtasks (surgical risk prediction and mitigation, triage classification, wait time regression, etc.).
• STARR ED Workflow: A real-world dataset with 100k+ Electronic Health Records (EHR).
Baselines: Monolithic NEAT, Modular NEAT, Transformer-based MoE, and EAI without modularity.
Hardware: (1) 4x NVIDIA RTX 3090 GPUs (total 96GB VRAM); (2) 50x Raspberry Pi 3B+ cluster (50GB total RAM, Ethernet interconnect).
Hyperparameters: \(k_m\) = 10-20, \(I\)=15, \(\eta\)=0.01, \(\lambda\)=0.1, \(\tau\)=0.85, generations=200.
4.2 Results
Efficiency Metrics
• Convergence Speed: Oscilon converged in 120 generations on average, 15x faster than monolithic NEAT (1800 generations) and 5x faster than Modular NEAT.
• Compute Usage: On RTX 3090s, peak VRAM was 19GB/GPU (vs. 40GB+ for baselines); runtime ~2 hours for MIMIC-III. On RPi 3B+, runtime ~8 hours post-optimization (e.g., reduced \(D_m\)), vs. infeasible for baselines.
• Scalability: With \(M=10\), Oscilon scaled linearly with \(P\), maintaining <10% overhead.
Accuracy and Determinism
• Task Performance: 94.2% accuracy on MIMIC-III workflows (vs. 89% for MoE, 91% for EAI alone), with MSE=12.3 for wait times (vs. 18.5 baseline).
• Hallucination Rate: Zero probabilistic errors, as measured by consistency across 100 runs (vs. 5-10% in transformers).
• Ablation Study: Removing modularity increased runtime by 4x; disabling parallelism by 3x. Varying \(k_m\) showed optimal at 15 for balance.
Hardware profiling confirmed RTX 3090 suitability for sparse ops, while RPi challenges were addressed via quantization (FP16) and selective synchronization.
5. Discussion
Oscilon’s modular design excels in dynamic environments like healthcare, where subtasks evolve independently. Its determinism ensures regulatory compliance, critical for FDA-approved AI.
Limitations include potential inter-module drift if dependencies are strong; we mitigate this with periodic global fitness checks. On low-power hardware like RPi 3B+, network latency remains a bottleneck, suggesting hybrid CPU-GPU setups for future work.
Extensions could incorporate adaptive reinforcement learning for auto-decomposition or federated learning for privacy-preserving healthcare data.
6. Conclusion
Oscilon advances the field of modular EAI by enabling efficient, deterministic AI on accessible hardware. By integrating EAI with parallelism and decomposition, it unlocks new possibilities for edge-based applications in healthcare and beyond, fostering more inclusive AI development.
