OpenEye: A Scalable Open-Source Hardware Accelerator for DNNs

Abstract

The increasing computational complexity of deep neural network inference poses significant challenges for efficient hardware acceleration on embedded platforms, particularly with respect to resource consumption and scalability. This work presents OpenEye, a scalable and sparsity-aware FPGA-based hardware accelerator designed to efficiently execute common neural network operations such as convolutions, dense layers, and pooling. OpenEye is based on a highly parameterizable architecture composed of clusters of processing elements interconnected by a streaming-based dataflow. The paper provides a detailed explanation of the internal operation of the accelerator, including data movement, buffering strategies, control logic, and the coordination between clusters and PEs. The architecture natively supports sparse weights and activations, enabling the efficient processing of sparse data without unnecessary computations or memory accesses. A key design property of OpenEye is its scalability: the number of clusters and processing elements can be varied to adapt the accelerator to different performance and resource constraints. The design achieves a near-linear scaling of routing and interconnect overhead with increasing PE counts, which is essential for maintaining efficiency on large FPGA devices. To evaluate scalability across different design points, multiple OpenEye configurations with varying cluster and PE sizes were implemented on a Xilinx ZU19EG FPGA. Representative neural network operations, including convolutional, fully connected, and pooling layers, were used to analyze resource utilization, execution latency, and scalability behavior. The results show favorable trade-offs between performance and resource consumption across the explored configurations.