Brain-Inspired Neuromorphic Chips for Ultra-Low-Power AI
We design neuromorphic integrated circuits that combine memory, computation and learning directly in hardware, enabling real-time intelligent processing at very low power.
The Engineering Challenge: AI Hardware Is Power Hungry
Current AI accelerators rely on high energy consumption and external training infrastructure. This limits their use in edge systems, defence platforms, robotics and embedded healthcare devices where power, latency and size are constrained.
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Our Technical Approach: Hardware-Embedded Learning
MindSilicaAI develops a neuromorphic chip architecture inspired by neural signalling in the human brain.
By co-designing CMOS circuits with memristive devices, we enable:
Event-Driven Parallel Processing
Processes information only when signals occur, reducing wasted energy.
Multiple operations run simultaneously for faster response times.
On-Chip Adaptive Learning
Learning mechanisms are embedded directly in hardware.
The system adapts in real time without external retraining.
Reduced Data Movement
Memory and computation are co-located on the chip.
Minimises energy loss and latency caused by constant data transfer.
Real-Time Edge Operation
Operates independently without reliance on cloud infrastructure.
Enables low-latency intelligence in power-constrained environments.
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