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相关概念视频

Understanding Memory01:19

Understanding Memory

558
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
558
Long-Term Memory01:18

Long-Term Memory

217
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...
217
System of Memory01:23

System of Memory

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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...
6.3K
Implicit Memories01:24

Implicit Memories

156
Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...
156
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

894
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

250
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
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用新兴的记忆技术优化内存深度学习.

Zhehui Wang, Tao Luo, Rick Siow Mong Goh

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    此摘要是机器生成的。

    新兴的记忆技术 (EMT) 提供了高效的内存深度学习,但受到不稳定的影响. 我们开发了优化技术来克服EMT.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 硬件工程 硬件工程

    背景情况:

    • 内存深度学习通过在数据存储的地方处理数据来提高效率,从而降低能源和时间成本.
    • 新兴的内存技术 (EMT) 为内存计算带来了密度,能量和性能方面的进一步增长.
    • 对EMT的一个关键挑战是其内在的不稳定性,导致数据读取波动和深度学习模型中的潜在准确性损失.

    研究的目的:

    • 解决内存深度学习中新兴记忆技术 (EMT) 的不稳定性.
    • 开发数学优化技术,以减轻由EMT波动引起的准确性损失.
    • 提高使用EMT的内存深度学习模型的精度和能源效率.

    主要方法:

    • 提出了三个新的数学优化技术,专门用于抵消EMT的不稳定性.
    • 将这些技术集成到内存深度学习框架中.
    • 进行实验以评估对模型准确性和能源效率的影响.

    主要成果:

    • 提出的优化技术成功克服了EMT固有的不稳定性问题.
    • 对大多数经过测试的深度学习模型,实现了最先进的 (SOTA) 准确度的完全恢复.
    • 与现有的SOTA方法相比,在能源效率方面至少有数量级的改进.

    结论:

    • 数学优化可以有效地解决由不稳定的新兴内存技术引起的准确性退化.
    • 开发的技术可以实现高度准确和节能的内存深度学习系统.
    • 这项工作为人工智能硬件中EMT的更强大和更高性能应用铺平了道路.