Waymo, a subsidiary of Google, has begun mass production of its self-developed dedicated chip for the latest generation of autonomous taxis, aimed at enhancing response and navigation capabilities in complex urban environments.

The chip is an application-specific integrated circuit (ASIC) with a computing power exceeding 1,000 TOPS, which means over one trillion operations per second; based on this metric, its performance is comparable to NVIDIA's latest autonomous driving systems.

Waymo states that this ASIC handles the front-end processing of sensor data, cleaning and fusing information from cameras, LiDAR, and radar, which is then processed by the vehicle's computing platform to understand and respond to the environment, thereby accelerating the autonomous vehicle's processing speed regarding road participants and urban conditions.

The chip is manufactured using TSMC's 5-nanometer process. Waymo has not disclosed the cost per chip, production capacity, foundry order amounts, chip area, or specific power consumption parameters; while 5-nanometer is not TSMC's most advanced node, it is still widely used for high-performance chip mass production.

Waymo has not completely replaced external suppliers with its self-developed chip. Its computing architecture still includes partners such as AMD, Micron, NVIDIA, Samsung, SanDisk, Socionext, and TSMC; the self-developed ASIC is just one part of the onboard computing system, and specific component roles have not been disclosed.

In terms of market mechanisms, Waymo has integrated high-frequency, low-latency, and highly customizable perception processing into its self-developed chip, which can reduce long-term reliance on general-purpose autonomous driving computing platforms and dilute design costs as the fleet expands; TSMC benefits from the demand for custom chip manufacturing, while external chip vendors like NVIDIA have not been completely replaced, but their share in the future value of Waymo's fleet computing power may be under pressure. The focus of competition will shift from merely purchasing peak computing power to achieving system-level efficiency among sensors, computing, power consumption, reliability, and cost for each vehicle.

Source: Public Information