Improving Low-Latency Spiking Neural Networks through Explicit Modeling of Residual Error in Artificial Neural Networks

Core Concepts

Explicitly modeling the residual error between artificial neural networks (ANNs) and converted spiking neural networks (SNNs) as additive noise can effectively reduce the performance gap under ultra-low-latency conditions.

Abstract

The paper proposes a new approach to improve the performance of low-latency ANN-SNN conversion by explicitly modeling the residual error between the source ANN and the converted SNN. The key insights are:

The authors analyze the sources of conversion errors, including clipping error, quantization error, and residual error. They find that the residual error, which is caused by the inability of integrate-and-fire (IF) neurons to respond to residual membrane potentials beyond the range from resting potential to threshold, is a major factor limiting the performance of low-latency converted SNNs.

To address this issue, the authors introduce a "Noisy Quantized" (NQ) activation function that incorporates additive Gaussian noise to the quantized ANN activation. This noise is designed to compensate for the residual error, effectively reducing the gap between the ANN and the converted SNN.

The authors propose a layer-wise error-compensating strategy to automatically adjust the noise intensity for each activation layer based on the validation set. This allows the noise to be tailored to the specific characteristics of each layer.

Experiments on the CIFAR-10 and CIFAR-100 datasets show that the proposed method outperforms state-of-the-art ANN-SNN conversion methods, especially under ultra-low-latency conditions (e.g., 2-4 time steps). For example, the authors achieve 93.72% top-1 accuracy on CIFAR-10 with just 2 time steps, significantly better than previous approaches.

The training overhead introduced by the noise induction is minimal, making the proposed method efficient and practical for deployment on neuromorphic hardware.

Stats

The paper does not provide specific numerical data points to support the key claims. However, it presents several figures and tables that illustrate the performance improvements achieved by the proposed method compared to state-of-the-art ANN-SNN conversion techniques.

Quotes

"The challenge of low-latency ANN-SNN conversion arises from conversion errors, which have been identified by previous studies [29; 1], resulting in a performance gap under low-latency conditions."
"We find that the conversion loss for low-latency SNN primarily stems from residual errors between quantized ANNs and converted SNNs."
"Explicitly modeling the residual error as a Gaussian noise with a zero mean and integrating the noise into the quantized activation of the source ANN during training, aiming to compensate for the gap between the source ANN and the converted SNN."

Key Insights Distilled From

Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs

by Zhipeng Huan... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17456.pdf

Deeper Inquiries

How can the proposed method be extended to handle more complex network architectures and larger-scale datasets?

The proposed method of explicit residual error modeling can be extended to handle more complex network architectures and larger-scale datasets by incorporating adaptive noise modeling techniques. For more complex architectures, such as deep neural networks with multiple layers, the noise intensity can be personalized for each layer to better capture the residual error distribution. Additionally, for larger-scale datasets, the noise intensity can be adjusted based on the specific characteristics of the dataset to optimize the conversion process. Furthermore, the hierarchical error-compensating strategy can be enhanced to dynamically adjust the noise intensity during training to adapt to the complexity and scale of the network and dataset.

What are the potential limitations or drawbacks of the explicit residual error modeling approach, and how can they be addressed?

One potential limitation of the explicit residual error modeling approach is the need for manual tuning or validation to determine the optimal noise intensity for each layer. This process can be time-consuming and may require additional computational resources. To address this limitation, automated techniques such as hyperparameter optimization or reinforcement learning algorithms can be employed to dynamically adjust the noise intensity during training. Additionally, incorporating uncertainty estimation methods can help quantify the uncertainty in the residual error modeling and provide more robust and reliable results.

Can the insights from this work be applied to improve the training and inference of directly trained spiking neural networks, beyond just the ANN-SNN conversion scenario?

Yes, the insights from this work can be applied to improve the training and inference of directly trained spiking neural networks beyond just the ANN-SNN conversion scenario. By incorporating explicit modeling of residual errors and adaptive noise modeling techniques, directly trained spiking neural networks can benefit from more accurate and efficient training processes. The noise modeling approach can help enhance the robustness and generalization of spiking neural networks, leading to improved performance on various tasks. Additionally, the hierarchical error-compensating strategy can be utilized to optimize the training of directly trained spiking neural networks and improve their overall efficiency and accuracy.