Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks

Codes and checkpoints will be updated SOON! [Paper]

Key contributions

Our "Adaptive Calibration" framework leverages biologically-inspired burst-firing mechanisms, already supported by commercial neuromorphic hardware like Intel's Loihi 2 and Synsense's Xylo. By developing a training-free optimization algorithm, we automatically determine optimal burst-firing configurations based on each layer's sensitivity characteristics, improving both efficiency and performance. Key features include:

• Training-free ANN-to-SNN Conversion: Outperforms existing methods with minimal setup time (0.09 hours), eliminating the need for costly retraining while maintaining superior accuracy-energy balance.
• Energy-efficient converted SNNs: Delivers remarkable energy reductions across benchmark datasets (70.1% on CIFAR-10, 60.3% on CIFAR-100, and 43.1% on ImageNet) while preserving high accuracy.
• Comprehensive Tasks/Architecture Support: Demonstrates exceptional versatility through extensive validation across diverse tasks (2D/3D classification, event-driven processing, object detection, and segmentation) and modern architectures (ResNet, VGG, and Vision Transformers).

*Hardware implementation code will be available in subsequent works.

Running the Code

1. Pre-training ANN on Neuromorphic Datasets:

python main_train_cifardvs.py --dataset cifar10dvs --arch resnet18

• --dataset: Specifies the dataset to be used, including cifar10dvs, ncaltech101, ncars, action recognition.

2. SNN Calibration with Burst-Spike Technique:

python main_train_cifardvs.py --dataset cifar10dvs --arch resnet18 --T 8 --calib light --maxspike 4

• --T: timestep of SNN.
• --calib: calibration method, light as default .
• --maxspike: maximum number of burst-spikes.

3. SNN Calibration with Burst-Spike Reallocation Technique:

python main_train_cifardvs.py --dataset cifar10dvs --arch resnet18 --T 8 --calib light \
--maxspike 4 --search --maxspike_ratio 1.0 --initialspike 8 --desired_spike 4 --minspike 1

• --search: Enables the search for optimal burst-spike reallocation.
• --maxspike_ratio: the factor ratio of energy budget.
• --initialspike: the initial number of burst-spike.
• --desired_spike 4: the target number of burst-spikes.
• --minspike 1: the minimum number of spikes allowed.

4. SNN Calibration with Sensitivity Spike Compression Technique:

python main_train_cifardvs.py --dataset cifar10dvs --arch resnet18 --T 8 --calib light \
--maxspike 4 --search_threshold --threshold_ratio 1.0

• --search_threshold: Activates the search for the optimal threshold based on sensitivity.
• --threshold_ratio 1.0: the threshold ratio for spike compression.