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’s rapid growth, powered by energy-hungry GPUs, has sparked a sustainability crisis driven by the inefficiency of digital computing’s von Neumann architecture. To overcome this, innovators are reviving analog and neuromorphic computing, which process data directly in memory, eliminating energy-intensive data movement. Companies like Vellex Computing are leading this shift, enabling ultra-efficient, brain-inspired chips that bring powerful, sustainable AI to edge devices worldwide.