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相关概念视频

System of Memory01:23

System of Memory

7.2K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
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Working Memory01:24

Working Memory

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Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
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Long-Term Memory01:18

Long-Term Memory

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Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
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Traumatic Memory01:20

Traumatic Memory

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Emotionally traumatic events often lead to memories that are exceptionally vivid and enduring, sometimes persisting with remarkable clarity throughout an individual's life. A classic example of this phenomenon is a person who survives a car accident. Even years later, they may recall every detail of the event with startling accuracy — the screeching of the tires, the jarring impact, and the acrid smell of burning rubber. Such vividness contrasts sharply with how an individual...
560
Repressed Memory01:16

Repressed Memory

505
Repressed memories are a psychological phenomenon where memories of traumatic events are unconsciously blocked from a person's awareness. This process occurs as a defense mechanism, protecting the mind from the emotional impact of distressing or painful experiences. For example, a person who has experienced childhood trauma may grow up with no conscious recollection of the event. In such cases, the memories are thought to be buried deep within the subconscious, inaccessible to the conscious...
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Immunological Memory01:23

Immunological Memory

15.1K
Immunological memory, a pivotal pillar of the adaptive immune system, is responsible for the body's ability to remember and respond more swiftly and effectively to previously encountered pathogens. This remarkable feature is what makes vaccines so effective in preventing diseases.
What is Immunological Memory?
Immunological memory is an integral function of the immune system that allows it to recognize and react more rapidly and effectively to pathogens previously encountered. This feature...
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相关实验视频

Updated: Jan 23, 2026

Translating Extracellular Electron Transfer Activities with Organic Electrochemical Transistors
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Translating Extracellular Electron Transfer Activities with Organic Electrochemical Transistors

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基于电化学随机访问内存的内置量子化与内存-晶体管传输单元,用于边缘应用程序.

Zhen Yang1, Yuxiang Yang1, Baiqian Wang2

  • 1New Cornerstone Science Laboratory, Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
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PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的突触单元,用于神经网络中高效的内存计算. 它使低精度AI的现场重量定量成为可能,大大降低了边缘设备的能源消耗.

关键词:
这就是ECRAM.边缘计算是一种边缘计算.在内存计算中的计算.重量量化量化的量化.

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相关实验视频

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机工程 计算机工程
  • 人工智能的人工智能

背景情况:

  • 使用非挥发性突触阵列进行内存计算,可以提高神经网络的能效.
  • 目前的设备主要是加速矩阵-矢量乘法,由于不同的训练/推理需求,复杂化混合单元集成.

研究的目的:

  • 开发一个紧的突触单元用于神经网络中低精度量子化计算.
  • 允许在现场进行近似的重量定量,而不需要额外的编程,同时保留并行矩阵向量乘法 (MVM) 功能.

主要方法:

  • 离子非挥发性内存-晶体管合集成用于一个紧的突触单元.
  • 利用细胞的物理电气特性来实现内在的定量化功能.

主要成果:

  • 在二进制神经网络中实现了与理想定量化可比的分类准确性.
  • 证明支持低精度的持续学习和减轻灾难性遗忘.
  • 基于ECRAM和RRAM的单元在4Mb规模的数字平台上显示出显著的能源消耗优势 (25.51×和4.84×).

结论:

  • 开发的突触单元为低精度边缘训练提供了一个强大的现场定量化框架.
  • 允许对二进制/三进制大语言模型进行高效的计算.
  • 为更节能的人工智能硬件提供了一条途径.