Dynamic Sparse Training Gets a Boost with SparseOpt

Dynamic Sparse Training (DST) isn't living up to its promise of speed. The process is supposed to trim down computation, but it's often a slow grind to match the efficiency of dense training. The main culprit? Batch Normalization (BN). It turns out, BN could be putting the brakes on sparse training.

The DST Bottleneck

Here's what the benchmarks actually show: Despite DST's potential, it converges significantly slower than dense training. We're talking about comparable training times to hit the same accuracy levels. That's not what anyone signed up for. It's a problem that needs fixing if DST is to be truly competitive.

The reality is, Batch Normalization, a popular tool in neural network training, might be more of a hindrance than a help DST. The interaction between BN and sparse layers isn't just suboptimal, it's a roadblock.

Enter SparseOpt

This is where SparseOpt steps in. It's a new sparsity-aware optimizer designed to address these issues. The architecture matters more than the parameter count, and SparseOpt is built with this in mind. It's not just about maintaining sparsity, but about doing so intelligently.

The numbers tell a different story with SparseOpt in play. Experiments on ResNet models using datasets like CIFAR-100 and ImageNet show consistently faster convergence rates. Add to that improved generalization, and SparseOpt starts looking like a real contender for speeding up DST.

Why Should You Care?

The implications here are significant. Faster convergence and better generalization mean more efficient use of resources, a key concern in training large models. This isn't just a technical detail. it's about making DST practically viable.

But here's the pointed question: Could SparseOpt make DST not just viable, but preferable? If it continues to show results, SparseOpt might very well become the go-to optimizer for those dealing with DST's current limitations.

Strip away the marketing and you get a clearer picture: Current normalization layers are holding DST back. With SparseOpt, there's a chance to leapfrog those limitations. It's a significant step, but whether it’s enough to fully tilt the scales in favor of DST remains to be seen. Still, SparseOpt is a move in the right direction.