CIR-NoC: Accelerating CNN Inference Through In-Router Computation During Network Congestion

Traditional Network-on-Chip (NoC) architectures frequently suffer from network congestion when processing complex Convolutional Neural Network (CNN) models, primarily due to the massive data volume and computation-intensive tasks. To address this issue, this paper proposes a new NoC-based CNN acceleration architecture. The core innovation involves integrating computational units into routers, thereby exploiting network congestion periods for in-router computations and alleviating the impact of NoC congestion on CNN inference efficiency. Focusing on activation operations as a representative case, we conduct experiments with three CNN models: LeNet, AlexNet, and ResNet. Experimental results demonstrate that, compared to the conventional VC-based router baseline, the proposed architecture achieves inference cycle reductions of 5.72%–6.64%, 5.12%–5.53%, and 4.53%–5.30% for the three models under varying NoC configurations (4×4, 8×8, and 16×16 mesh topologies). These results validate that the proposed architecture significantly improves inference efficiency. Additionally, we design two optimization strategies based on the architecture and provide a systematic analysis of their effectiveness and applicability across different CNN models.