cuDNN is a library of efficient implementations for deep learning primitives, designed to optimize deep learning workloads on parallel processors. Deep learning is computationally intensive, and optimizing kernels is challenging. cuDNN provides optimized routines for deep learning, similar in intent to BLAS, enabling efficient performance and memory usage. It integrates easily into existing frameworks like Caffe, improving performance and reducing memory consumption. The library supports forward and backward propagation for convolution, pooling, and activation functions, with flexible data layouts and strides. It provides a low-level API for efficient integration with deep learning frameworks. cuDNN's convolution routines are optimized for performance, using minimal auxiliary memory and supporting various use cases, including small mini-batch sizes. cuDNN's implementation is based on matrix multiplication, which is highly optimized. It avoids the need for large auxiliary memory by using a tiled approach, reducing memory traffic and improving performance. The library is designed to be portable across different GPU architectures, providing consistent performance without requiring code re-tuning. cuDNN has been integrated into frameworks like Caffe and Baidu's PADDLE, improving performance and memory efficiency. In Caffe, integrating cuDNN improved training performance by 36% on a standard model. cuDNN is also used in other domains beyond image processing, such as speech and language, due to its flexibility and efficiency. Future work includes expanding cuDNN to support 1D and 3D convolutions, local receptive fields, and multi-GPU training. The library is available for use and welcomes feedback. cuDNN provides a reliable and efficient solution for deep learning, enabling researchers to focus on higher-level tasks while benefiting from optimized performance and memory usage.cuDNN is a library of efficient implementations for deep learning primitives, designed to optimize deep learning workloads on parallel processors. Deep learning is computationally intensive, and optimizing kernels is challenging. cuDNN provides optimized routines for deep learning, similar in intent to BLAS, enabling efficient performance and memory usage. It integrates easily into existing frameworks like Caffe, improving performance and reducing memory consumption. The library supports forward and backward propagation for convolution, pooling, and activation functions, with flexible data layouts and strides. It provides a low-level API for efficient integration with deep learning frameworks. cuDNN's convolution routines are optimized for performance, using minimal auxiliary memory and supporting various use cases, including small mini-batch sizes. cuDNN's implementation is based on matrix multiplication, which is highly optimized. It avoids the need for large auxiliary memory by using a tiled approach, reducing memory traffic and improving performance. The library is designed to be portable across different GPU architectures, providing consistent performance without requiring code re-tuning. cuDNN has been integrated into frameworks like Caffe and Baidu's PADDLE, improving performance and memory efficiency. In Caffe, integrating cuDNN improved training performance by 36% on a standard model. cuDNN is also used in other domains beyond image processing, such as speech and language, due to its flexibility and efficiency. Future work includes expanding cuDNN to support 1D and 3D convolutions, local receptive fields, and multi-GPU training. The library is available for use and welcomes feedback. cuDNN provides a reliable and efficient solution for deep learning, enabling researchers to focus on higher-level tasks while benefiting from optimized performance and memory usage.