一种基于掩码机制的非对称填充与卷积的算子融合优化方法及系统
By fusing asymmetric padding and convolution operators into a single kernel on a GPGPU, and utilizing masking mechanisms and Triton auto-tuning, the performance bottleneck of asymmetric padding and convolution on GPGPU is solved, thereby improving the model's performance and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HARBIN INST OF TECH
- Filing Date
- 2025-09-08
- Publication Date
- 2026-07-17
AI Technical Summary
The independent computation of asymmetric padding and convolution at the GPGPU level leads to excessive memory consumption and data transfer overhead, becoming a performance bottleneck for deep learning models, especially in applications that process high-resolution images or real-time video streams.
By using a masking mechanism to integrate asymmetric padding and convolution operators into a single kernel, the computation graph is optimized using the masking mechanism, achieving end-to-end collaborative optimization, reducing memory bandwidth overhead and kernel startup times, and combining Triton's automatic tuning capabilities to automatically adapt to hardware and input parameters.
It significantly reduces global memory access traffic on GPGPUs, lowers CPU overhead and synchronization latency, improves model inference throughput and reduces latency, and adapts to diverse GPGPU deployment environments.
Smart Images
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