Design of tensor core based on "Ventus" GPGPU

To meet the growing demands for computational power and versatility in neural networks, a tensor processor is designed based on the open-source project " Ventus" GPGPU. The tensor processor can accelerate convolution and general matrix multiplication operations. This study analyzes existing tensor processor design schemes and their corresponding algorithms and compares their performance differences with direct convolution calculations. Subsequently, a novel tensor processor design based on a three-dimensional multiplication tree structure is proposed. The proposed design is deployed on the Xilinx VCU128 development board. The tensor processor operates at a frequency of 222 MHz on the VCU128 development board. Additionally, an exponential operation unit is developed to aid in neural network operations. The frequency is 159 MHz on the VCU128 development board. The functionality of the tensor processor is verified using assembly language programming, and the results demonstrated a significant reduction in expected execution time after introducing the tensor processor. These findings contribute to the advancement of hardware acceleration for deep learning applications and provide a foundation for further research in this field.