The invention addresses the challenge of simultaneous read and write operations in Spiking Neural Networks (SNN) that utilize a cross-bar architecture. This architecture typically requires small amplitude biases for reading and large amplitude biases for writing, which complicates concurrent operations. The invention proposes a solution involving a Frequency Division Multiplexer (FDM) and a filter to manage and separate read and write pulses, enabling efficient simultaneous operations.
Type of IP
Faculty
Category of Patent
Department
This technology can lead to significant advancements in AI, making systems more energy-efficient and capable of real-time learning. It can drive innovations in various fields, from healthcare (e.g., brain-computer interfaces) to autonomous systems (e.g., self-driving cars), ultimately contributing to the development of smarter and more efficient technologies.
- RRAM Cross-Bar Based Synaptic Array: Utilizes Resistive Random-Access Memory for efficient synaptic operations.
- Frequency Division Multiplexer (FDM): Separates read and write pulses by frequency.
- Filter: Removes write pulse components while retaining read pulse components to drive the post-synaptic neuron.
- Pulse Components: Read pulses are high-frequency signals, while write pulses are low-frequency and segmented into fast spike and slower time-keeping portions.
- Capacitor Integration: A capacitor in parallel with RRAM to block low-frequency components of the write pulse.
- Simultaneous Read/Write Operations: Efficiently handles both operations concurrently without interference. Low Power Consumption: Avoids the need for power-hungry clocks.
- Biologically Realistic: Supports asynchronous, clock-less operations similar to biological neural networks.
- Improved Accuracy: Minimizes read errors by separating and filtering pulse components effectively.
3
The technology is implemented in a GlobalFoundries 45nm technology. The prototype is demonstrated to show area and power efficiency. The system-level demonstration of the neural network using the LIF neuron is ongoing.
- Artificial Intelligence and Machine Learning: Enhancing SNNs for low-power, brain-like learning and recognition tasks.
- Neuromorphic Computing: Developing hardware that mimics the neural structure of the human brain.
- Memory Storage Technology: Innovating new methods for efficient data storage and retrieval.
- Semiconductor Industry: Manufacturing advanced integrated circuits and components for neuromorphic systems.
- Cognitive Computing: Systems that require real-time learning and adaptation.
- Robotics: Enhancing autonomous learning and decision-making capabilities.
- Signal Processing: Efficient data processing in neuromorphic systems.
- Advanced AI Research: Developing new algorithms and hardware for brain-like computation.