At a glance
| Booth | T9404 |
| Country | US |
| Website | www.vellex.ai |
Company profile
Vellex Computing, a Stanford spinout backed by the U.S. National Science Foundation and Department of Energy, is an analog semiconductor company making AI training radically more energy-efficient. Today, training AI models can require megawatts of power and massive cloud compute costs, putting it out of reach for most edge applications. We solve the optimization problem at the heart of AI training by using analog circuits and our analog compute architecture rather than brute-force digital computation. This proprietary architecture has demonstrated 17,000x speedup over conventional techniques while operating at milliwatt power levels. Starting with edge AI in energy, industrial IoT, robotics, and remote sensing, Vellex enables devices to learn continuously and securely in real-time, without any cloud reliance. With active commercial pilots and a growing patent portfolio, Vellex is unlocking the next generation of adaptive, cloud-free industrial intelligence.
Exhibits
Develops analog semiconductors and an analog compute architecture for AI training, using analog circuits to solve the core optimization problem at milliwatt power levels with high speed. Supports edge-AI devices in energy, industrial IoT, robotics, and remote sensing with continuous, secure, real-time learning without cloud reliance.
Capabilities and products
- analog semiconductor company making AI training energy-efficient
- Stanford spinout backed by the U.S. National Science Foundation
- analog circuits and analog compute architecture
- 17,000x speedup over conventional techniques
- operating at milliwatt power levels
- edge AI in energy, industrial IoT, robotics and remote sensing
- continuous real-time learning without cloud reliance
- active commercial pilots and a growing patent portfolio