neuromorphic+computing
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KAIST researchers developed a novel ultra-low power memory for neuromorphic computing
A team of Korean researchers is making headlines by developing a new memory device that can be used to replace existing memory or used in implementing neuromorphic computing for next-generation artificial intelligence hardware for its low processing costs and its ultra-low power consumption.
KAIST (President Kwang-Hyung Lee) announced on April 4th that Professor Shinhyun Choi's research team in the School of Electrical Engineering has developed a next-generation phase change memory* device featuring ultra-low-power consumption that can replace DRAM and NAND flash memory.
☞ Phase change memory: A memory device that stores and/or processes information by changing the crystalline states of materials to be amorphous or crystalline using heat, thereby changing its resistance state.
Existing phase change memory has the problems such as expensive fabrication process for making highly scaled device and requiring substantial amount of power for operation. To solve these problems, Professor Choi’s research team developed an ultra-low power phase change memory device by electrically forming a very small nanometer (nm) scale phase changeable filament without expensive fabrication processes. This new development has the groundbreaking advantage of not only having a very low processing cost but also of enabling operating with ultra-low power consumption.
DRAM, one of the most popularly used memory, is very fast, but has volatile characteristics in which data disappears when the power is turned off. NAND flash memory, a storage device, has relatively slow read/write speeds, but it has non-volatile characteristic that enables it to preserve the data even when the power is cut off.
Phase change memory, on the other hand, combines the advantages of both DRAM and NAND flash memory, offering high speed and non-volatile characteristics. For this reason, phase change memory is being highlighted as the next-generation memory that can replace existing memory, and is being actively researched as a memory technology or neuromorphic computing technology that mimics the human brain.
However, conventional phase change memory devices require a substantial amount of power to operate, making it difficult to make practical large-capacity memory products or realize a neuromorphic computing system. In order to maximize the thermal efficiency for memory device operation, previous research efforts focused on reducing the power consumption by shrinking the physical size of the device through the use of the state-of-the-art lithography technologies, but they were met with limitations in terms of practicality as the degree of improvement in power consumption was minimal whereas the cost and the difficulty of fabrication increased with each improvement.
In order to solve the power consumption problem of phase change memory, Professor Shinhyun Choi’s research team created a method to electrically form phase change materials in extremely small area, successfully implementing an ultra-low-power phase change memory device that consumes 15 times less power than a conventional phase change memory device fabricated with the expensive lithography tool.
< Figure 1. Illustrations of the ultra-low power phase change memory device developed through this study and the comparison of power consumption by the newly developed phase change memory device compared to conventional phase change memory devices. >
Professor Shinhyun Choi expressed strong confidence in how this research will span out in the future in the new field of research saying, "The phase change memory device we have developed is significant as it offers a novel approach to solve the lingering problems in producing a memory device at a greatly improved manufacturing cost and energy efficiency. We expect the results of our study to become the foundation of future electronic engineering, enabling various applications including high-density three-dimensional vertical memory and neuromorphic computing systems as it opened up the possibilities to choose from a variety of materials.” He went on to add, “I would like to thank the National Research Foundation of Korea and the National NanoFab Center for supporting this research.”
This study, in which See-On Park, a student of MS-PhD Integrated Program, and Seokman Hong, a doctoral student of the School of Electrical Engineering at KAIST, participated as first authors, was published on April 4 in the April issue of the renowned international academic journal Nature. (Paper title: Phase-Change Memory via a Phase-Changeable Self-Confined Nano-Filament)
This research was conducted with support from the Next-Generation Intelligent Semiconductor Technology Development Project, PIM AI Semiconductor Core Technology Development (Device) Project, Excellent Emerging Research Program of the National Research Foundation of Korea, and the Semiconductor Process-based Nanomedical Devices Development Project of the National NanoFab Center.
2024.04.04 View 4400 -
Professor Shinhyun Choi’s team, selected for Nature Communications Editors’ highlight
[ From left, Ph.D. candidates See-On Park and Hakcheon Jeong, along with Master's student Jong-Yong Park and Professor Shinhyun Choi ]
See-On Park, Hakcheon Jeong, Jong-Yong Park - a team of researchers under the leadership of Professor Shinhyun Choi of the School of Electrical Engineering, developed a highly reliable variable resistor (memristor) array that simulates the behavior of neurons using a metal oxide layer with an oxygen concentration gradient, and published their work in Nature Communications. The study was selected as the Nature Communications' Editor's highlight, and as the featured article posted on the main page of the journal's website.
Link : https://www.nature.com/ncomms/
[ Figure 1. The featured image on the main page of the Nature Communications' website introducing the research by Professor Choi's team on the memristor for artificial neurons ]
Thesis title: Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing.
( https://doi.org/10.1038/s41467-022-30539-6 )
At KAIST, their research was introduced on the 2022 Fall issue of Breakthroughs, the biannual newsletter published by KAIST College of Engineering.
This research was conducted with the support from the Samsung Research Funding & Incubation Center of Samsung Electronics.
2022.11.01 View 5606 -
Neuromorphic Memory Device Simulates Neurons and Synapses
Simultaneous emulation of neuronal and synaptic properties promotes the development of brain-like artificial intelligence
Researchers have reported a nano-sized neuromorphic memory device that emulates neurons and synapses simultaneously in a unit cell, another step toward completing the goal of neuromorphic computing designed to rigorously mimic the human brain with semiconductor devices.
Neuromorphic computing aims to realize artificial intelligence (AI) by mimicking the mechanisms of neurons and synapses that make up the human brain. Inspired by the cognitive functions of the human brain that current computers cannot provide, neuromorphic devices have been widely investigated. However, current Complementary Metal-Oxide Semiconductor (CMOS)-based neuromorphic circuits simply connect artificial neurons and synapses without synergistic interactions, and the concomitant implementation of neurons and synapses still remains a challenge. To address these issues, a research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering implemented the biological working mechanisms of humans by introducing the neuron-synapse interactions in a single memory cell, rather than the conventional approach of electrically connecting artificial neuronal and synaptic devices.
Similar to commercial graphics cards, the artificial synaptic devices previously studied often used to accelerate parallel computations, which shows clear differences from the operational mechanisms of the human brain. The research team implemented the synergistic interactions between neurons and synapses in the neuromorphic memory device, emulating the mechanisms of the biological neural network. In addition, the developed neuromorphic device can replace complex CMOS neuron circuits with a single device, providing high scalability and cost efficiency.
The human brain consists of a complex network of 100 billion neurons and 100 trillion synapses. The functions and structures of neurons and synapses can flexibly change according to the external stimuli, adapting to the surrounding environment. The research team developed a neuromorphic device in which short-term and long-term memories coexist using volatile and non-volatile memory devices that mimic the characteristics of neurons and synapses, respectively. A threshold switch device is used as volatile memory and phase-change memory is used as a non-volatile device. Two thin-film devices are integrated without intermediate electrodes, implementing the functional adaptability of neurons and synapses in the neuromorphic memory.
Professor Keon Jae Lee explained, "Neurons and synapses interact with each other to establish cognitive functions such as memory and learning, so simulating both is an essential element for brain-inspired artificial intelligence. The developed neuromorphic memory device also mimics the retraining effect that allows quick learning of the forgotten information by implementing a positive feedback effect between neurons and synapses.”
This result entitled “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse” was published in the May 19, 2022 issue of Nature Communications.
-Publication:Sang Hyun Sung, Tae Jin Kim, Hyera Shin, Tae Hong Im, and Keon Jae Lee (2022) “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse,” Nature Communications May 19, 2022 (DOI: 10.1038/s41467-022-30432-2)
-Profile:Professor Keon Jae Leehttp://fand.kaist.ac.kr
Department of Materials Science and EngineeringKAIST
2022.05.20 View 9799