🧠 Project Overview We designed and implemented a two-dimensional convolution processor that performs kernel-based filtering on input image data. The processor supports different kernel sizes and can ...

The 2D convolution unit is configurable to support 2D convolution operations with different sizes of input image matrix and kernel filter. The architecture can reduce memory access time and power as ...

When benchmarking 2D depthwise convolutions on an NVIDIA H200, I observed that TensorFlow’s implementation is noticeably slower and consumes more power compared to PyTorch. Using a kernel-level ...

A high performance digital architecture for computing 2D convolution utilizing the quadrant symmetry of the kernels is proposed in this paper. Pixels in the four quadrants of the kernel region with ...