自动驾驶之心·2025-07-26 13:30Core Viewpoint - Pure GPU can achieve basic functions of low-level autonomous driving but has significant shortcomings in processing speed, energy consumption, and efficiency, making it unsuitable for meeting the requirements of high-level autonomous driving [39][41]. Group 1: GPU Limitations - Pure GPU can handle certain parallel computing tasks required for autonomous driving, such as sensor data fusion and image recognition, but it was originally designed for graphics rendering, leading to limitations in performance [5][10]. - Early tests with pure GPU solutions showed significant latency issues, such as an 80 ms delay in target detection while driving at 60 km/h, which poses safety risks [5][6]. - The data processing capacity of L4 autonomous vehicles generates approximately 5-10GB of data per second, requiring multiple GPUs to work together, which increases power consumption and reduces vehicle range by about 30% [6][7]. Group 2: NPU and TPU Advantages - NPU is specifically designed for neural network computations, featuring a large number of MAC (Multiply-Accumulate) units that optimize matrix multiplication and accumulation operations, significantly improving efficiency compared to GPU [12][15]. - TPU, developed by Google, utilizes a pulsed array architecture that enhances data reuse and reduces external memory access, achieving a data reuse rate three times higher than that of GPU [14][19]. - In terms of energy efficiency, NPU can achieve an energy efficiency ratio that is 2.5 to 5 times better than GPU, with lower power consumption for the same AI computing power [34][41]. Group 3: Cost and Performance Comparison - The cost of high-end GPUs can be significantly higher than that of NPUs; for instance, the NVIDIA Jetson AGX Xavier costs around $800 per unit, while the Huawei Ascend 310B is approximately $300 [35][36]. - To achieve similar AI computing power, a pure GPU solution may require multiple units, leading to a total cost that is 12.5% of that of a Tesla FSD chip that includes NPU [35][36]. - In practical scenarios, a pure GPU solution consumes significantly more energy compared to a mixed NPU+GPU solution, resulting in a reduction of vehicle range by approximately 53 km per 100 km driven [34][41]. Group 4: Future Trends - The future of autonomous driving technology is likely to favor a hybrid approach that combines NPU and GPU, leveraging the strengths of both to enhance processing efficiency while maintaining software compatibility and reducing costs [40][41].