Quantizing Spiking Neural Networks: Beyond Just Accuracy

Quantization in Spiking Neural Networks (SNNs) is becoming a hot topic, especially for those looking to deploy these networks on hardware with limited resources. But here's the catch: while most discussions zero in on accuracy, we might be missing a bigger picture. It's not just about how precise these networks are but how they behave when quantized.

The Real Deal with Firing Behavior

Today's focus on accuracy overlooks something important: the firing behavior of a quantized network versus its full-precision counterpart. In practice, this is essential for deployment. Why? Because the way a network fires determines effective sparsity, state storage, and event-processing load. The demo is impressive. The deployment story is messier. If you're only checking accuracy, you're not seeing the whole picture.

A New Diagnostic: Earth Mover's Distance

There's a new tool in town for evaluating these networks, Earth Mover's Distance (EMD). EMD looks beyond accuracy to see if quantization alters firing distributions. And guess what? It often does. Using SEW-ResNet models on datasets like CIFAR-10 and CIFAR-100, researchers found that uniform quantization can lead to a drift in firing behavior, even if accuracy stays the same.

Contrast that with learned quantization methods like LQ-Net, which tend to keep firing distributions closer to their full-precision baselines. This suggests that behavior preservation should be valued just as much as accuracy when evaluating SNNs.

Why Should You Care?

So, why does this matter? In production, this looks different. Imagine deploying a network on a real-time system. If you're not accounting for firing behavior, you might be in for some nasty surprises. Think of state storage requirements and event-processing loads skyrocketing. It's not just about getting good numbers on paper, it's about ensuring that these numbers translate to real-world performance.

Here's where it gets practical: if you're working on deploying SNNs, start asking the right questions. Don't just check if your model hits the accuracy mark. Ask if it behaves the way you expect it to when quantized. The real test is always the edge cases. Are we ready to make behavior an official criterion alongside accuracy?