Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks (2505.05375v2)

Abstract: Recently, spiking neural networks (SNNs), deployed on neuromorphic chips, provide highly efficient solutions on edge devices in different scenarios. However, their ability to adapt to distribution shifts after deployment has become a crucial challenge. Online test-time adaptation (OTTA) offers a promising solution by enabling models to dynamically adjust to new data distributions without requiring source data or labeled target samples. Nevertheless, existing OTTA methods are largely designed for traditional artificial neural networks and are not well-suited for SNNs. To address this gap, we propose a low-power, neuromorphic chip-friendly online test-time adaptation framework, aiming to enhance model generalization under distribution shifts. The proposed approach is called Threshold Modulation (TM), which dynamically adjusts the firing threshold through neuronal dynamics-inspired normalization, being more compatible with neuromorphic hardware. Experimental results on benchmark datasets demonstrate the effectiveness of this method in improving the robustness of SNNs against distribution shifts while maintaining low computational cost. The proposed method offers a practical solution for online test-time adaptation of SNNs, providing inspiration for the design of future neuromorphic chips. The demo code is available at github.com/NneurotransmitterR/TM-OTTA-SNN.

Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks

The paper entitled "Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks" addresses the critical challenge of adapting Spiking Neural Networks (SNNs) to distribution shifts in real-time, specifically when deployed on neuromorphic hardware. This research introduces a framework called Threshold Modulation (TM) for Online Test-Time Adaptation (OTTA), specifically tailored for SNNs. The need for such a framework arises due to the marked differences in operational paradigms between SNNs and traditional Artificial Neural Networks (ANNs), particularly in their interactions with neuromorphic hardware, where power efficiency and adaptability are crucial.

Spiking Neural Networks and Neuromorphic Computing

SNNs, inspired by biological neuron activity, utilize discrete spikes to transmit information, providing significant energy advantages over traditional ANNs due to their inherent event-driven processing. This makes them well-suited for deployment on neuromorphic chips, such as Loihi and TrueNorth, particularly in low-power edge computing scenarios. However, like ANNs, SNNs must effectively handle distribution shifts post-deployment to maintain their robustness and utility in dynamic environments.

Online Test-Time Adaptation

OTTA facilitates dynamic model adjustments during inference, handling streaming data effectively without labeled target samples or revisiting source data. Although OTTA strategies have been developed for ANNs, they often do not translate effectively to SNNs due to differences in network architecture and operational constraints, such as the immutability of weights and challenges in modifying input/output or internal layers dynamically when deployed on neuromorphic systems.

Threshold Modulation Framework

The proposed TM framework targets this adaptation gap by rethinking the adaptation mechanism at the neuronal level, aligning it with the constraints of neuromorphic hardware. The core of this approach is modifying neuronal firing thresholds based on membrane potential statistics, enabling adaptation without altering the model weights or output directly. The TM method enhances SNN robustness against distribution shifts by recalibrating neuron thresholds, inspired by biological processing, and facilitating real-time adaptation without extensive computational overhead.

Contributions of the Paper:

1. Novel Adaptation Framework: The introduction of a TM-based framework offers a low-power adaptation strategy suited for the deployment characteristics and constraints of SNNs on neuromorphic hardware.
2. Membrane Potential Batch Normalization (MPBN): This modification of conventional batch normalization techniques aligns well with SNNs' needs, focusing on the dynamics of membrane potentials and facilitating adaptable yet stationary network parameters once deployed.
3. Experimental Validation: The authors demonstrate the framework's effectiveness across multiple benchmark datasets, including CIFAR-10/100-C and ImageNet-C, showcasing improved robustness to common corruptions.

Experimental Results

The framework's efficacy is exemplified by significant reductions in classification error on standard benchmarks when using the TM module compared to non-adaptive baselines. Notably, this performance was achieved with minimal increases in theoretical energy consumption, aligning with the framework's goal of maintaining computational efficiency that is compatible with the operational constraints of neuromorphic chips.

Implications and Future Directions

The TM framework positions itself as a potential standard for SNN adaptations, encouraging the development of neuromorphic systems that incorporate dynamic model adaptation on the fly. The flexibility offered by TM in recalibrating thresholds opens new avenues for increasing the resilience and robustness of SNNs facing unforeseen environmental changes after deployment.

Future research can explore integrating similar mechanisms in broader application contexts, emphasizing diverse sensorimotor tasks where SNNs thrive due to their low-power consumption and real-time processing capabilities. Moreover, expanding the adaptability of such systems to include more complex neuronal dynamics or hybrid architectures could push the frontier of neuromorphic computing, making intelligent systems more versatile and ubiquitous.