Auto-Spikformer: Spikformer Architecture Search (2306.00807v1)

Abstract: The integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their biological properties. Recent advancements in SNN architecture, such as Spikformer, have demonstrated promising outcomes by leveraging Spiking Self-Attention (SSA) and Spiking Patch Splitting (SPS) modules. However, we observe that Spikformer may exhibit excessive energy consumption, potentially attributable to redundant channels and blocks. To mitigate this issue, we propose Auto-Spikformer, a one-shot Transformer Architecture Search (TAS) method, which automates the quest for an optimized Spikformer architecture. To facilitate the search process, we propose methods Evolutionary SNN neurons (ESNN), which optimizes the SNN parameters, and apply the previous method of weight entanglement supernet training, which optimizes the Vision Transformer (ViT) parameters. Moreover, we propose an accuracy and energy balanced fitness function $\mathcal{F}_{AEB}$ that jointly considers both energy consumption and accuracy, and aims to find a Pareto optimal combination that balances these two objectives. Our experimental results demonstrate the effectiveness of Auto-Spikformer, which outperforms the state-of-the-art method including CNN or ViT models that are manually or automatically designed while significantly reducing energy consumption.

• The paper introduces an automated Spikformer architecture search that balances high accuracy with reduced energy consumption.
• It integrates evolutionary tuning of SNN parameters with a joint fitness function to optimize both neural performance and efficiency.
• Empirical results on CIFAR datasets demonstrate superior accuracy and lower energy usage compared to state-of-the-art SNN models.

Overview of "Auto-Spikformer: Spikformer Architecture Search"

The paper presents Auto-Spikformer, an innovative approach to transform Spiking Neural Networks (SNNs) by leveraging Transformer Architecture Search (TAS). The core motivation is to address the energy inefficiency found in recent SNN architectures such as Spikformer, which while effective, can suffer from excessive resource consumption due to redundant channels and blocks. To overcome this, the authors propose an efficient automated method that balances performance and energy efficiency through strategic architecture search and optimization.

Technical Contributions

The paper's notable contributions can be distilled into several key areas:

1. Transformer Architecture Search for SNNs: This paper introduces a method for one-shot Transformer Architecture Search tailored specifically to spiking neural networks, a departure from traditional applications of TAS which are generally focused on Vision Transformers (ViTs) in standard artificial neural network contexts.
2. Evolutionary SNN Neurons (ESNN): A novel approach, ESNN employs evolutionary algorithms to optimize parameters internal to SNN neurons, such as the membrane potential threshold, decay rates, and time-steps. This represents the first application of evolutionary strategies for tuning these parameters, aiming to enhance both the accuracy and efficiency of the network.
3. Joint Fitness Function: The authors propose an accuracy and energy balanced fitness function, $\mathcal{F}_{AEB}$, to evaluate potential architectures. This function holistically considers both energy consumption and accuracy, guiding the search towards Pareto optimal solutions that strike a balance between these two critical factors.
4. Empirical Validation: The Auto-Spikformer method is empirically validated on the CIFAR datasets, demonstrating superior performance compared to both state-of-the-art manually and automatically designed models. The model achieves notable improvements in accuracy with reduced energy consumption, validating the proposed search strategy and optimization techniques.

Implications and Future Directions

The introduction of Auto-Spikformer has several implications for both the practical deployment of SNN-based systems and theoretical advancements in architecture search methodologies:

• Energy Efficiency of SNNs: By effectively reducing energy consumption through optimized architecture search, Auto-Spikformer addresses one of the key challenges in deploying SNNs for biologically inspired AI applications, where energy constraints often prove prohibitive.
• Scalability and Adaptability: The methodology can potentially be adapted to larger datasets and more complex tasks beyond CIFAR, including neuromorphic computing applications where efficient processing and energy use are paramount.
• Cross-Pollination of Techniques: The successful integration of TAS within SNNs suggests further investigation into cross-disciplinary techniques, potentially redefining approach paradigms across both natural and artificial neural network research.

Conclusion

Auto-Spikformer represents a significant stride in the automated design and optimization of spiking neural networks. By embedding TAS, evolutionary strategies, and an innovative fitness function into the search process, it opens avenues for more efficient and scalable SNN architectures. Future expansions could explore its application in real-world neuromorphic datasets and extend its principles to broader classes of neural networks, further bridging the gap between biologically plausible models and deployable intelligent systems.