Latest from Vellex

When field-deployed AI models encounter unexpected conditions, they degrade. The traditional fix relies on sending massive amounts of raw data back to the cloud for retraining. This article explores the severe connectivity constraints—from limited bandwidth to crippling latency—that make cloud dependency a critical flaw for edge devices. Discover how Vellex Computing eliminates this bottleneck entirely by using analog semiconductors to train models directly on-device, enabling continuous, real-time adaptation for drones, satellites, and industrial IoT without requiring an internet connection.

AI models deployed on edge hardware degrade over time as field conditions change. The traditional solution, cloud retraining, is costly, consumes significant power, and relies on connectivity that remote systems lack. This article examines the compounding constraints of power, memory, and connectivity that have historically prevented on-device AI training. Discover how Vellex Computing leverages analog circuit dynamics to enable continuous, real-time parameter updates directly on edge hardware, removing cloud dependency for autonomous robotics, satellite platforms, and industrial IoT.

Analog vs. Digital technology is a decades-old debate, but which one is actually better for the future of tech? In this comprehensive guide, we break down the core differences between continuous analog waves and discrete digital steps. Discover the unique pros and cons of each system, learn which architecture is best suited for your specific industry—from professional audio to cutting-edge AI—and explore how modern hardware engineers are blending both into powerful, ultra-efficient Hybrid (A/D/A) systems to overcome the massive energy limitations of today's devices.