MULiN: A NoC-DNN Accelerator Based on In-Network Multiplication

The rapid advancement of deep learning has driven the remarkable success of Deep Neural Networks (DNNs) across various domains. However, the computational and data transmission complexity of DNN models presents significant challenges to traditional hardware architectures. Network-on-Chip (NoC)-based DNN accelerators have emerged as a promising solution to address these challenges. To improve DNN inference speed and alleviate the impact of network congestion on computational efficiency, this paper presents a NoC architecture with in-network multiplication. By embedding multiplication units within the NoC routers, partial computations are offloaded during periods of network congestion, thereby enhancing overall system efficiency. We develop a cycle-accurate NoC-DNN simulator. Under NoC scales of 8×8 and 16×16, the inference speeds of LeNet and AlexNet-like models are improved 10.7%, 6.9%, 16.3%, and 15.5%, respectively.