Can Sparsity Make AI Models More Accurate?

Introduces Sparse Iso-FLOP Transformation (Sparse-IFT), a technique to increase AI model accuracy by increasing model size and inducing unstructured sparsity while maintaining iso-FLOPs per layer. Demonstrates accuracy gains of up to 3.5% on CV models and 0.4 perplexity improvement on LLMs. Highlights Cerebras's hardware and software co-design for accelerating unstructured sparsity, enabling efficient application of techniques like Sparse-IFT.