Convolutional Neural Networks (CNNs) as a quintessential representation of deep learning, are the most commonly used neural networks in tasks such as computer vision. However, convolution operations typically account for over 90% of the runtime in CNNs, becoming a bottleneck for performance. Additionally, due to the complexity of current hardware and the diversity of workloads, specific optimizations in previous work often lack performance portability. To address this problem, the author introduces BlazerML, an open-source convolution computation library based on auto-generated code templates from TVM, capable of automatically generating high-performance convolution implementations for any input shape. BlazerML is implemented based on the Winograd algorithm, known for its high performance in fast convolution algorithms. Experimental results demonstrate that BlazerML significantly outperforms current state-of-the-art open-source libraries. On x86 CPUs, running common deep learning network forward inferences, it is faster by 1.18—2.47 times, 1.18—2.27 times, and 1.01—1.66 times compared to OnnxRuntime, MNN, and the TVM community version, respectively. On ARM CPUs, for single-layer inference of common deep learning networks, it surpasses ACL and FastConv by 1.26—6.11 times and 1.04—4.28 times, respectively.