Latest from Vellex

Today's smart devices and edge devices are constrained by static inference models that cannot adapt to changing real-world conditions, leading to intelligence decay. On-device training overcomes traditional power and memory barriers, enabling continuous, ultra-low-power learning directly on battery-constrained hardware. By eliminating energy-heavy cloud transmissions, localized training enables hyper-personalized, secure, and self-healing AI, creating a foundation for truly autonomous and adaptive edge devices.

Modern EV and autonomous vehicle safety is limited by digital-first architectures that introduce latency, power, and signal-quality constraints. Analog intelligence enables continuous, ultra-low-power computation directly on raw sensor signals before digitization, improving response time, robustness, and always-on safety. By enhancing sensor quality and reducing front-end latency, analog computing complements digital AI and forms a hybrid, physics-aligned foundation for safer vehicles.

AI and high-performance computing are hitting an energy wall as digital architectures consume unsustainable power for data movement and training. Analog computing offers a breakthrough - processing information through continuous physical dynamics instead of binary logic, enabling up to 100× energy efficiency and real-time performance. By computing with physics itself, analog systems redefine performance metrics and pave the path toward sustainable, high-efficiency intelligence.