Predictor-Assisted Evolutionary Neural Architecture Search for Spiking Neural Networks
Spiking Neural Networks (SNNs) represent a biologically inspired computing paradigm that transmits information through discrete spikes, offering improved biological interpretability and superior energy efficiency. Despite these advantages, the design of high-performance SNN architectures remains a major challenge, as existing structures rely heavily on manual engineering and expert intuition. This research addresses this issue by proposing a fully automated method to discover optimal SNN architectures using a predictor-assisted evolutionary neural architecture search framework, aiming to enhance performance while reducing computational cost and energy consumption.
Limitations of Manual SNN Architecture Design
Traditional approaches to SNN architecture development depend largely on manual design strategies, making them inflexible and difficult to scale. These human-crafted architectures often suffer from limited adaptability, restricted search space exploration, and reliance on expert knowledge, which can hinder innovation. By highlighting these limitations, the study emphasizes the need for a systematic and automated method to generate architectures capable of achieving superior performance without extensive human intervention.
PESNN Framework and Evolutionary Neural Architecture Search
The proposed PESNN model builds upon the Evolutionary Neural Architecture Search (ENAS) framework, leveraging evolutionary algorithms to automate SNN structure discovery. This approach enables the exploration of large and complex architectural spaces without predefined constraints. Through iterative evolution, mutation, and evaluation, PESNN can progressively refine candidate architectures, ultimately identifying designs that offer both accuracy and efficiency. This automated workflow represents a significant shift from traditional hand-crafted SNN development.
Variable-Length Coding Strategy for Automated Architecture Design
A key innovation of this research is the introduction of a variable-length coding strategy based on spatial channel adaptation. Unlike previous NAS methods for SNNs that still incorporate partial manual design, this strategy fully automates the definition of architectural depth, width, and structural variations. By enabling flexible and dynamic architecture representation, the proposed encoding method supports richer design variations and enhances the overall search capability, leading to more optimized and adaptable network models.
Performance Predictor for Accelerated Fitness Evaluation
Fitness evaluation in neural architecture search can be extremely computationally expensive, especially for SNNs that require simulation over numerous timesteps. To address this challenge, PESNN integrates a performance predictor that estimates model accuracy without exhaustive training. This predictor significantly accelerates the search process by reducing evaluation time while maintaining reliable performance estimation. As a result, the method achieves faster convergence and allows for more extensive exploration of potential architectures.
Multi-Objective Optimization for Reduced Spikes and Power Consumption
In addition to improving accuracy, the PESNN incorporates multi-objective optimization to minimize both spike counts and energy consumption—critical metrics for neuromorphic computing. By jointly optimizing accuracy and efficiency, the system identifies architectures that offer high-quality classification results with fewer spikes and reduced power usage. Experiments on CIFAR-10, CIFAR-100, and DVS-CIFAR-10 demonstrate that PESNN delivers superior performance while achieving optimal trade-offs between energy efficiency and computational cost.
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