Scaling AI
Infrastructure
Beyond Silicon
Making Analog Computing Work
The world is moving beyond the limits of digital computing. GPU scaling is slowing while data centers consume enormous energy levels, costing hundreds of billions annually as demands from AI, simulation, and knowledge discovery grow exponentially.
Analog systems like neuromorphic, biological, memristor, and photonic computing promise 100-1000x efficiency gains that could transform computing. However, after $100B+ invested globally over the past decade, analog solutions remain confined to labs.
The core problem: You can't scale what you can't trust. Analog computing is inherently unreliable, with the same inputs producing different outputs and errors compounding across networks. Enterprises need verification to trust systems, and they need trust before deploying anything at scale.
ZEDEN is developing the control layer that provides real-time verification and active stabilization for analog systems. This will become the trust foundation that is essential to making analog's revolutionary efficiency gains scalable in the real world.
Mission Statement
Technical Approach
ZEDEN's discrete control architecture is designed to provide the reliability guarantees that will be crucial in deploying next-generation computing for mission-critical applications where traditional silicon hits efficiency limits.
Signal Verification
Real-time verification methods ensure continuous substrate signals produce intended computational results. This enables trust in biological, photonic, and memristor systems for the first time in computing history.
State Control
Active intervention and correction systems for substrate drift, noise, and failure modes to maintain computational integrity across extended operation periods.
Universal Interface
Standardized communication protocols that abstract away substrate-specific complexities. This eventually allows applications to treat biological neurons, photonic processors, and analog chips as interchangeable compute resources.
Applications
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Dramatically cuts costs in multi-billion dollar markets
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Enables reliable and highly efficient commercial use of analog systems
AI Inference Optimization
Reducing LLM energy costs through verified analog processing while maintaining accuracy.
Adaptive Signal Processing
40-60% energy reduction for beamforming across 5G/6G, satellite, automotive LIDAR, and medical imaging.
Edge Intelligence Systems
