A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration (2106.06984v1)

Abstract: Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only replacing the ReLU activation to spike activation while keeping the parameters intact. Perhaps surprisingly, in this work we show that a proper way to calibrate the parameters during the conversion of ANN to SNN can bring significant improvements. We introduce SNN Calibration, a cheap but extraordinarily effective method by leveraging the knowledge within a pre-trained Artificial Neural Network (ANN). Starting by analyzing the conversion error and its propagation through layers theoretically, we propose the calibration algorithm that can correct the error layer-by-layer. The calibration only takes a handful number of training data and several minutes to finish. Moreover, our calibration algorithm can produce SNN with state-of-the-art architecture on the large-scale ImageNet dataset, including MobileNet and RegNet. Extensive experiments demonstrate the effectiveness and efficiency of our algorithm. For example, our advanced pipeline can increase up to 69% top-1 accuracy when converting MobileNet on ImageNet compared to baselines. Codes are released at https://github.com/yhhhli/SNN_Calibration.

• The paper introduces a novel layer-wise calibration approach that mitigates conversion errors by addressing flooring and clipping issues during ANN-to-SNN transitions.
• It details Light and Advanced pipeline options to balance computational efficiency with enhanced accuracy for spiking network deployment.
• Empirical evaluations on large-scale ImageNet datasets show accuracy gains up to 69%, underscoring the method’s potential for energy-efficient neuromorphic applications.

Towards Efficient Spiking Neural Networks Calibration

The paper "A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration" discusses significant improvements in converting Artificial Neural Networks (ANN) to Spiking Neural Networks (SNN) through a novel calibration technique. The adoption of SNNs, inspired by biological neuron spiking behavior, provides significant energy efficiency advantages over traditional ANNs, especially on neuromorphic hardware.

Traditionally, converting an ANN to an SNN involved substituting the ReLU activations with spike activations, which often resulted in suboptimal performance without parameter adjustment. The authors of this paper propose a "SNN Calibration" method that refines network parameters during ANN-to-SNN conversion, narrowing the gap between activation distributions of source ANNs and target SNNs. This calibration process only requires a limited number of training samples and is computationally inexpensive.

Key contributions of the paper include:

1. Error Analysis in Conversion: By decomposing the conversion error into flooring and clipping errors, the paper lays down a theoretical framework for understanding error propagation during ANN-to-SNN conversion. The findings emphasize the necessity to calibrate network parameters to mitigate these errors effectively.
2. Layer-wise Calibration Algorithm: The paper introduces a robust layer-wise calibration approach that adjusts weights, biases, and membrane potentials across the network. The algorithm proposes two pipelines—Light and Advanced—each balancing accuracy against computational resource requirements. The Light Pipeline is quick and resource-efficient, while the Advanced Pipeline offers superior accuracy through comprehensive parameter calibration.
3. Empirical Validation: The effectiveness of the SNN Calibration method is affirmed through extensive evaluations on large-scale ImageNet datasets using state-of-the-art architectures, including MobileNet and RegNet. The results exhibit substantial improvements, with accuracy gains of up to 69% in some scenarios, reinforcing the efficiency and practicality of the approach.

The implications of this research are multifaceted. Practically, efficient SNN calibration allows the deployment of neural networks in energy-constrained environments, such as mobile devices and edge computing systems. The calibration technique also opens up avenues for optimizing other neuromorphic tasks where latency and power efficiency are paramount. Theoretically, this work contributes to the growing understanding of ANN-to-SNN conversion mechanisms and sets a benchmark for future studies aiming to enhance SNN usability.

Looking ahead, the exploration of advanced calibration techniques using fewer data samples or improved thresholding methods could further streamline the conversion process. Additionally, this work might stimulate further research into novel SNN architectures tailored for low-latency performance, potentially leading to more feasible and scalable applications of spiking neural networks in real-world scenarios.