Thomas Henrique Schmitz

A Multi-Core Winograd F(2,3) FPGA Accelerator with Low On-Chip Memory Footprint for VGG16 Convolution

Convolutional layers remain one of the main computational bottlenecks in deep neural networks, motivating specialized hardware accelerators for efficient inference. Winogradbased FPGA accelerators reduce the cost of $3 \times 3$ convolutions, but their efficiency depends heavily on how transformed activations and weigh...