[Paper] Functional Stability of Software-Hardware Neural Network Implementation The NeuroComp Project

Source: arXiv - 2512.04867v1

Overview

The NeuroComp project introduces a hardware‑centric twist on neural‑network robustness: instead of relying on software tricks like Dropout during training, it builds redundancy directly into the physical implementation of each neuron. By deploying every neuron on its own ESP32 microcontroller, the system can tolerate individual node failures without degrading overall functionality—an appealing property for edge‑AI devices that must run reliably in noisy or harsh environments.

Key Contributions

• Neuron‑level hardware redundancy: Each artificial neuron is instantiated on a separate ESP32, turning a single‑point‑of‑failure architecture into a fault‑tolerant mesh.
• Functional stability analysis: Formal definitions and metrics for “functional stability” are introduced, quantifying how many failed neurons a network can sustain while preserving inference accuracy.
• Comparison with Dropout: The paper contrasts the proposed hardware redundancy with the classic Dropout regularizer, showing that the former protects runtime operation rather than just training.
• Prototype implementation: A complete end‑to‑end prototype (including firmware, communication protocol, and a small‑scale neural net) demonstrates the concept on real hardware.
• Guidelines for scaling: Design rules and trade‑off analyses (power, latency, and cost) for extending the approach to larger networks are provided.

Methodology

1. Neuron Partitioning: The target neural network (e.g., a multilayer perceptron) is decomposed so that each neuron, together with its weights and activation function, lives on an independent ESP32 board.
2. Inter‑node Communication: Neurons exchange activations over a lightweight wireless mesh (ESP‑Now) or wired UART bus, forming a distributed forward‑propagation pipeline.
3. Fault Injection & Detection: During experiments, individual ESP32 units are deliberately powered off or corrupted to emulate hardware failures. The system monitors missing messages and automatically bypasses dead nodes.
4. Stability Metric: The authors define a stability threshold (k) – the maximum number of simultaneous neuron failures that keep the network’s output within a pre‑specified error bound (e.g., ≤ 2 % drop in classification accuracy).
5. Benchmarking: The prototype is evaluated on standard datasets (MNIST, CIFAR‑10) and compared against a monolithic software implementation and a Dropout‑regularized version.

Results & Findings

• Resilience up to 15 % neuron loss: For a 100‑neuron hidden layer, the distributed network maintained ≥ 95 % of its baseline accuracy even when 15 neurons (randomly selected) were disabled.
• Latency overhead: The added communication latency averaged 0.8 ms per layer—acceptable for many edge‑AI use cases but higher than a pure software stack.
• Power consumption: Running each neuron on an ESP32 consumed ~80 mW, leading to a total of ~8 W for a 100‑neuron layer; however, the ability to shut down failed nodes reduced overall draw by ~5 % in fault scenarios.
• Comparison with Dropout: While Dropout improved training robustness, it offered no protection against runtime hardware faults. The hardware‑redundant design filled this gap without needing retraining.

Practical Implications

• Edge devices in harsh environments: Sensors, drones, and industrial IoT nodes that operate in high‑temperature, vibration, or radiation zones can benefit from neuron‑level redundancy to keep AI inference alive despite component wear‑out.
• Safety‑critical systems: Autonomous vehicles or medical devices can adopt this architecture to meet stringent reliability standards (e.g., ISO 26262) by providing graceful degradation rather than catastrophic failure.
• Modular AI hardware design: The approach encourages a “plug‑and‑play” ecosystem where developers can add or replace neuron modules on the fly, simplifying maintenance and upgrades.
• Fault‑tolerant AI services: Cloud‑edge hybrid deployments could offload critical inference to a distributed hardware mesh, reducing reliance on centralized GPUs that may become bottlenecks or single points of failure.

Limitations & Future Work

• Scalability concerns: Replicating each neuron on a separate microcontroller quickly becomes cost‑ and space‑inefficient for deep networks with thousands of neurons.
• Communication bottlenecks: As network depth grows, the cumulative latency and bandwidth demands of inter‑node messaging may exceed the capabilities of ESP‑Now or UART links.
• Energy budget: While individual nodes are low‑power, the aggregate consumption can be prohibitive for battery‑operated devices.
• Future directions: The authors suggest exploring hierarchical redundancy (grouping neurons into clusters), leveraging more capable low‑power ASICs, and integrating error‑detecting codes into the communication layer to further reduce overhead.

Bottom line: The NeuroComp project demonstrates that hardware redundancy at the neuron level is a viable path to functional stability for AI systems operating outside the pristine conditions of data centers. While not a silver bullet for all deep‑learning workloads, it opens a new design space for resilient edge AI—an area that developers and hardware architects should keep an eye on.

Authors

• Bychkov Oleksii
• Senysh Taras

Paper Information

• arXiv ID: 2512.04867v1
• Categories: cs.AR, cs.NE
• Published: December 4, 2025
• PDF: Download PDF