让卷积计算更省内存，支持混合精度，在GPU上提速6倍以上。
Enabling Memory-efficient Im2win Convolution with Multi-precision Support on GPU CUDA and Tensor Cores
- 改进im2win结构，适配CUDA和Tensor核心的混合精度运算。
- 在12个CNN模型上达到最高6.4倍于传统方法的算力，内存仅用35%。
- 适合需要高吞吐、低内存占用的深度学习部署场景。
卷积是深度神经网络的主要计算瓶颈，其效率依赖算法与GPU硬件的紧密协同。现有GPU卷积方法存在内存开销大、缓存利用率低、核尺寸适应性差或数值不稳定等问题。本文将im2win范式——一种适用于所有核尺寸、具有连续内存访问特性的通用高效卷积方法——扩展至在CUDA核心上运行全精度、在张量核心上运行半精度。通过引入新型内核设计与优化技术，如锯齿形内存访问和异步数据搬运，im2win高效利用硬件加速的半精度矩阵乘加操作。在12个CNN基准测试中，im2win相比其CUDA核心实现最高提升2.8倍TFLOPS，比cuDNN高1.4倍，比cuBLAS基的GEMM卷积高6.4倍，且内存占用仅为后者的53%和35%。这些结果确立了im2win作为现代GPU架构下统一高效的卷积框架的地位。
原文摘要 · Abstract (English)
Convolution is a principal computational bottleneck in deep neural networks, and its efficiency depends on tight integration between algorithms and GPU hardware. Existing GPU convolution methods suffer from large memory overhead, poor cache utilization, limited effectiveness across kernel sizes, or numerical instability. This work extends the im2win paradigm -- a universal, memory-efficient convolution method with contiguous memory access for all kernel sizes -- to run efficiently in full precision on CUDA cores and half precision on tensor cores. By introducing new kernel designs and optimizations such as zig-zag memory access and asynchronous data movement, im2win efficiently exploits hardware-accelerated half-precision matrix multiply-accumulate operations. Across twelve CNN benchmarks, im2win achieves up to 2.8x higher TFLOPS than its CUDA core implementation, 1.4x higher than cuDNN, and 6.4x higher than GEMM-based convolution with cuBLAS, while using as little as 53% and 35% of their memory, respectively. These results establish im2win as a unified, high-performance convolution framework for modern GPU architectures.
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