Optimization of Convolution Operators Using Image-to-Column Transformation and SGEMM

Efficient convolution operations are vital for deep learning but are frequently computationally demanding. This paper introduces an optimization strategy leveraging the Image-to-Column (im2col) transformation in combination with Single-Precision General Matrix Multiplication (SGEMM) to reformulate convolution as matrix multiplication. The approach incorporates advanced CUDA techniques, such as shared memory utilization and tiling, to further enhance performance. Experimental results demonstrate runtime reductions of up to 95%, underscoring the method's effectiveness for large-scale datasets and its value in accelerating deep learning workloads.