The Stanford team’s work on Onyx represents a significant step toward addressing the growing energy and computational demands of AI models. By focusing on sparsity—a property where most parameters in neural networks are zero or near-zero—they’ve demonstrated that hardware can be re-architected to skip unnecessary calculations, drastically improving efficiency. This approach challenges the prevailing trend of simply scaling up AI models, which has led to diminishing returns in performance while e...