CAMVA: An Extension Architecture of CNN Accelerators for Multi-View Acceleration

Autopilot vehicles integrate additional views to capture comprehensive feature information, enhancing target detection accuracy. However, this integration imposes a higher computational burden on CNN accelerators in autopilot systems, potentially increasing the response latency of autopilot systems. It is a challenge for safety-critical autopilot systems. Traditionally, researchers have addressed this challenge by employing complex designs and processes to enhance the arithmetic capabilities of chips. In contrast, the paper proposes a simple technology that is an extension architecture of CNN accelerators for multiview acceleration (CAMVA). The extension architecture utilizes the characteristic of multi-view approximation to enhance the sparsity of input features and dynamically reuse the approximated feature extraction results, accelerating CNN accelerators. This paper uses multi-view autonomous driving datasets (KITTI and nuScenes) to create two tasks, and evaluates the impact of CAMVA on 2D and 3D object detection networks by simulating their data flows. Results of 2D experiments show that for 2D object detection, the accuracy of the KITTI task decreases by 2.29%~4.26%, while that of the nuScenes task decreases by 0.37%~1.11%. For 3D object detection, the accuracy of the KITTI task decreases by 1.97%~-0.22%. Then, the paper utilizes the pruning operation of CAMVA to create two subtasks, which are subsets of the KITTI with sparsity of 9.6% and 19.8%, respectively. It evaluates the performance, energy consumption, and RTL of CAMVA at the hardware architecture level based on the two subtasks. The results show that CAMVA enhances the performance of CNN accelerator by 1.04~1.08× and reduces energy consumption by 4.23%~8.01% in the sparsity 9.6% subtask; additionally, it improves performance by 1.13~1.34× and decreases energy consumption by 14.13%~25.97% in the sparsity 19.8% subtask. CAMVA increases CNN accelerator’;s area by a mere 0.75%.