Latest from Vellex

Wearable medical devices rely on pre-trained AI models to monitor health, but these models degrade as they encounter the wearer's unique physiological baselines. The traditional fix requires transmitting raw biometric data to a remote server, creating severe HIPAA compliance risks and draining constrained device batteries. This article explores how the power demands of digital processors keep AI training locked in data centers, and how Vellex Computing uses ultra-low-power analog circuits to move the retraining process directly onto the wearable, ensuring autonomous personalization without compromising patient privacy.

Autonomous drones rely on pre-trained AI models to navigate and inspect infrastructure, but these static models fail when field conditions diverge from training data. The traditional fix requires transmitting raw sensor data back to a remote server, consuming critical bandwidth and creating crippling latency. This article explores how the high power requirements of digital processors keep AI retraining locked in data centers, and how Vellex Computing uses milliwatt-level analog hardware to move the retraining process directly onto the drone, ensuring continuous adaptation in remote and contested environments.

Earth observation satellites rely on pre-trained AI models to process massive volumes of imagery on-orbit, but these static models degrade as hardware decays and atmospheric conditions change. The traditional fix requires transmitting gigabytes of raw training data back to Earth, consuming the limited downlink bandwidth meant for high-value intelligence. This article explores how the power constraints of digital processors keep AI retraining locked in data centers, and how Vellex Computing uses milliwatt-level analog circuits to move the retraining process directly onto the satellite, ensuring autonomous adaptation without the downlink bottleneck.