Today’s AI systems—like those behind self-driving cars or facial recognition—rely on massive digital computations using matrix math. As these systems grow more complex, they become slower and require enormous amounts of hardware, energy, and time, creating bottlenecks in speed and scalability.
Core Innovation
This invention proposes a radical shift: instead of using traditional digital circuits, it performs AI calculations using analog circuits. That means electrical signals (voltages) do the math—mimicking how the human brain works—rather than binary code in a processor.
Inventive Step
What makes this unique is that core AI functions like multiplying weights, adding biases, and applying decision rules (like the Sigmoid function) are all done using tiny, low-cost analog components. These are designed to fit together seamlessly and can be “cascaded” (linked like LEGO blocks) to handle even very large AI tasks in real time—without waiting for software to catch up.
Tangible Benefits
- Speeds up AI dramatically by bypassing slow digital processes.
- Reduces hardware needs by replacing bulky logic gates with minimal analog parts.
- Cuts power usage, making devices more energy-efficient.
- Scales effortlessly, handling large AI workloads across connected chips.
Broader Impact
This invention could transform industries—from robotics and autonomous vehicles to IoT devices—by making AI faster, cheaper, and more sustainable. It reduces the environmental footprint of AI computing and opens the door to real-time, low-cost intelligent systems that don’t rely on powerful cloud servers.