AI & Computing
Nvidia’s Always-On Chip Detects Faces in Less Than a Millisecond
Mar 18, 2026, 2:00 PM
出典: IEEE Spectrum AI
<img src="https://spectrum.ieee.org/media-library/close-up-of-a-woman-s-eyes-with-the-rest-of-her-face-obscured-by-scattered-pixels.jpg?id=65305602&width=2000&height=1500&coordinates=0%2C0%2C0%2C0"/><br/><br/><p>Always-on vision systems might be used in autonomous vehicles, robotics, or to help consumer electronics save power by turning screens off when no one’s around. But to be used in such a way, these systems need to minimize their own power consumption.</p><p>An always-on computer vision system developed by <a data-linked-post="2669201017" href="https://spectrum.ieee.org/nvidia-ai" target="_blank">Nvidia</a> researchers can detect human faces in less than a millisecond. The face detector, which is part of an chip that could be integrated into robots, autonomous vehicles, or laptops, saves power by storing all data locally and “racing to sleep” after detections. NVIDIA electrical engineer <a href="https://research.nvidia.com/person/ben-keller" rel="noopener noreferrer" target="_blank">Ben Keller</a> presented the system on 18 February at the <a href="https://www.isscc.org/" target="_blank">IEEE International Solid State Circuits Conference</a> in San Francisco.</p><h2>Efficient Vision Processing Technology</h2><p>According to the researchers, this kind of vision processing typically requires about 10 watts. But that’s too much power to leave a face detection system on continuously. The Nvidia system on chip (SoC) uses less than 5 milliwatts with a frame rate of 60 frames per second.</p><p>The system refreshes to process a new image every 16.7 milliseconds, and is only fully powered on for 5 percent of that time, says Keller. Within 787 microseconds, the SoC calls on a deep-learning accelerator to determine whether or not a human face is present, with about 99 percent accuracy.</p><p>The Nvidia team carefully designed the system to perform the detection rapidly and save power. Most parts of the SoC are powered off by default. A subsystem that uses less than 10 mW remains on. This subsystem is dubbed “Always-on Low-Power Accelerator,” or <a href="https://research.nvidia.com/publication/2026-02_alpha-vision-real-time-always-vision-processor-787ms-face-detection-latency" target="_blank">Alpha-Vision</a>. </p><p>The face detector uses a <a data-linked-post="2650272934" href="https://spectrum.ieee.org/biggest-neural-network-ever-pushes-ai-deep-learning" target="_blank">deep neural network</a> to recognize faces, which requires a lot of data—in other words, a potential power drain. To save power and speed up detections, all the necessary data is stored locally in a large area of SRAM adding up to 2 megabytes. The SRAM takes up a large footprint of the chip. To prevent SRAM leakage from dominating power usage, the SoC integrates a deep-learning accelerator and a near-memory processor to speedily turn the memory bank off. The researchers call this approach “race to sleep.”</p><p>The Nvidia team proposed several possible uses of such a system. A laptop computer integrating the face sensor could save energy by turning its display off when the user walks away, and then turning it back on when they return. The goal would be to provide a seamless experience—no need to type in a password. Keller says systems based on these designs might also be used to provide always-on vision for autonomous vehicles, drones, and robotics.</p>
Details
Always-on vision systems might be used in autonomous vehicles, robotics, or to help consumer electronics save power by turning screens off when no one’s around. But to be used in such a way, these systems need to minimize their own power consumption.
An always-on computer vision system developed by Nvidia researchers can detect human faces in less than a millisecond. The face detector, which is part of an chip that could be integrated into robots, autonomous vehicles, or laptops, saves power by storing all data locally and “racing to sleep” after detections. NVIDIA electrical engineer Ben Keller presented the system on 18 February at the IEEE International Solid State Circuits Conference in San Francisco.
Efficient Vision Processing Technology
According to the researchers, this kind of vision processing typically requires about 10 watts. But that’s too much power to leave a face detection system on continuously. The Nvidia system on chip (SoC) uses less than 5 milliwatts with a frame rate of 60 frames per second.
The system refreshes to process a new image every 16.7 milliseconds, and is only fully powered on for 5 percent of that time, says Keller. Within 787 microseconds, the SoC calls on a deep-learning accelerator to determine whether or not a human face is present, with about 99 percent accuracy.
The Nvidia team carefully designed the system to perform the detection rapidly and save power. Most parts of the SoC are powered off by default. A subsystem that uses less than 10 mW remains on. This subsystem is dubbed “Always-on Low-Power Accelerator,” or Alpha-Vision.
The face detector uses a deep neural network to recognize faces, which requires a lot of data—in other words, a potential power drain. To save power and speed up detections, all the necessary data is stored locally in a large area of SRAM adding up to 2 megabytes. The SRAM takes up a large footprint of the chip. To prevent SRAM leakage from dominating power usage, the SoC integrates a deep-learning accelerator and a near-memory processor to speedily turn the memory bank off. The researchers call this approach “race to sleep.”
The Nvidia team proposed several possible uses of such a system. A laptop computer integrating the face sensor could save energy by turning its display off when the user walks away, and then turning it back on when they return. The goal would be to provide a seamless experience—no need to type in a password. Keller says systems based on these designs might also be used to provide always-on vision for autonomous vehicles, drones, and robotics.
Related Knowledge
mentions
Always-On Vision Systems
Always-on vision systems are designed to continuously monitor environments for specific stimuli, such as human presence, while minimizing power consumption. These systems are crucial for applications in autonomous vehicles, robotics, and smart consumer electronics, enabling features like automatic screen activation or deactivation based on user presence.
mentions
Deep Learning Accelerators
Deep learning accelerators are specialized hardware designed to optimize the performance of deep learning algorithms. They significantly speed up the training and inference processes by providing the necessary computational power and memory bandwidth, making them essential for applications in computer vision, natural language processing, and more.