18299811. NEUROMORPHIC MEMORY ELEMENT SIMULTANEOUSLY IMPLEMENTING VOLATILE AND NON-VOLATILE FEATURE FOR EMULATION OF NEURON AND SYNAPSE simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
NEUROMORPHIC MEMORY ELEMENT SIMULTANEOUSLY IMPLEMENTING VOLATILE AND NON-VOLATILE FEATURE FOR EMULATION OF NEURON AND SYNAPSE
Organization Name
Inventor(s)
Sang Hyun Sung of Daejeon (KR)
Young Hoon Jung of Daejeon (KR)
NEUROMORPHIC MEMORY ELEMENT SIMULTANEOUSLY IMPLEMENTING VOLATILE AND NON-VOLATILE FEATURE FOR EMULATION OF NEURON AND SYNAPSE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18299811 titled 'NEUROMORPHIC MEMORY ELEMENT SIMULTANEOUSLY IMPLEMENTING VOLATILE AND NON-VOLATILE FEATURE FOR EMULATION OF NEURON AND SYNAPSE
Simplified Explanation
The disclosed patent application is about a neuromorphic memory element that can emulate neuronal and synaptic plasticity functions. Here is a simplified explanation of the abstract:
- The memory element consists of a first electrode and a second electrode.
- There is a first thin film layer between the first electrode and the second electrode, which emulates neuronal plasticity.
- The first thin film layer performs a volatile storage function based on the voltage difference between the first and second electrodes.
- There is a second thin film layer between the first thin film layer and the second electrode, which emulates synaptic plasticity.
- The second thin film layer performs a non-volatile storage function.
Potential Applications:
- Artificial intelligence: This neuromorphic memory element can be used in AI systems to mimic the plasticity of biological neurons and synapses, enabling more efficient and adaptable learning algorithms.
- Neuromorphic computing: The memory element can be integrated into neuromorphic chips to enhance their ability to process and store information in a brain-like manner.
- Robotics: By incorporating this memory element into robotic systems, it can improve their ability to learn and adapt to changing environments.
Problems Solved:
- Limited memory capabilities: The neuromorphic memory element addresses the limitations of traditional memory technologies by emulating the plasticity of biological neurons and synapses, allowing for more efficient and flexible storage of information.
- Lack of adaptability: This technology solves the problem of rigid memory structures by providing a memory element that can dynamically change its storage properties based on voltage differences, mimicking the adaptability of biological systems.
Benefits:
- Enhanced learning and processing capabilities: The ability to emulate neuronal and synaptic plasticity enables more efficient learning algorithms and improved processing capabilities in AI systems and neuromorphic computing.
- Flexibility and adaptability: The memory element's ability to perform volatile and non-volatile storage functions allows for dynamic changes in memory properties, making it more adaptable to different tasks and environments.
- Improved efficiency: By emulating the plasticity of biological systems, this technology offers the potential for more energy-efficient memory solutions compared to traditional approaches.
Original Abstract Submitted
Disclosed is a neuromorphic memory element, which includes a first electrode; a second electrode; a first thin film layer adjacent to the first electrode between the first electrode and the second electrode and that is configured to emulate a neuronal plasticity by performing a volatile storage function based on a voltage difference between the first electrode and the second electrode; and a second thin film layer between the first thin film layer and the second electrode and that is configured to emulate a synaptic plasticity by performing a non-volatile storage function.
