Molecular Neuromorphic Hardware
Brain-inspired AI chips: 14-bit precision, 200× less energy
Vision
To build the substrate for AI that learns continuously, locally, and at orders-of-magnitude lower energy than digital hardware.
Problem
AI compute is bottlenecked by energy: today's GPUs spend most of their power moving data between memory and processor, not computing, making AI expensive, slow, and impossible to run on-device.
Solution
Molecular memristor crossbars that fuse memory and computation in one physical medium, delivering 14-bit analog precision with orders-of-magnitude lower energy than digital AI hardware.
Potential
Edge AI silicon is a $50B+ market by 2030. Our platform targets every device that needs to learn locally: phones, drones, satellites, wearables, defence sensors; potentially billions of endpoints.
Target Beneficiaries
AI-hardware OEMs, defence and space agencies, edge-device manufacturers, healthcare wearables firms, and any nation seeking sovereign, low-power AI infrastructure beyond the cloud.
Current Users
Chip-level prototypes running CNN, ANN, LSTM and language model inference and training; validated on James Webb Space Telescope data; in active translation with DRDO and MeitY for 200-mm wafer-scale integration.
