Which library is used to accelerate inference in neural networks by leveraging sparsity in neurons?

The correct answer is cuSPARSELt. This library is specifically designed to accelerate inference in neural networks by taking advantage of sparsity in the weights and neurons. Sparsity refers to the phenomenon where a significant number of weights in a neural network are zero or can be effectively ignored, which can lead to reduced computational overhead and increased performance during inference.

cuSPARSELt is optimized to provide efficient operations that leverage this sparsity, resulting in faster execution times while minimizing memory usage. This is particularly relevant for deployment scenarios where efficiency is critical, making it a valuable tool for developers looking to optimize neural network inference.

The other libraries mentioned have different primary purposes: cuSPARSE is focused on sparse matrix operations, cuDNN is a GPU-accelerated library designed for deep learning operations (e.g., convolutions, activation functions), and cuBLAS is intended for dense linear algebra operations. While important in their respective contexts, they do not specifically address the acceleration of inference through sparsity as effectively as cuSPARSELt does.