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相关概念视频

MOS Capacitor01:25

MOS Capacitor

666
A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
666
Implicit Memories01:24

Implicit Memories

81
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...
81
Role of Cerebellum and Prefrontal Cortex in Memory01:14

Role of Cerebellum and Prefrontal Cortex in Memory

322
The cerebellum, while traditionally associated with motor control, also plays a crucial role in memory, particularly in procedural memory, which involves learning motor tasks that become automatic through repetition. For example, studies have shown that when the cerebellum is damaged, individuals or animals lose the ability to learn conditioned motor responses, such as the conditioned eye-blink response in classical conditioning experiments with rabbits. This study demonstrates the...
322

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相关实验视频

Updated: May 24, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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一个混合精度的memristor和SRAM内存计算AI处理器

Win-San Khwa1, Tai-Hao Wen2, Hung-Hsi Hsu1,2

  • 1Taiwan Semiconductor Manufacturing Company Limited (TSMC), Hsinchu, Taiwan, Republic of China.

Nature
|March 5, 2025
PubMed
概括

本研究介绍了一种用于AI边缘设备的新型混合精度异质内存计算 (CIM) 处理器. 它通过在不同的CIM架构和数字格式中智能分区任务来优化能源效率,准确性和速度.

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相关实验视频

Last Updated: May 24, 2025

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科学领域:

  • 计算机工程
  • 人工智能硬件
  • 节能计算

背景情况:

  • 人工智能边缘设备需要高精度,节能计算,大量存储和快速响应时间.
  • 使用同质架构 (memristor-CIM,SRAM-CIM) 或计算格式 (整数,浮点) 的传统内存计算 (CIM) 方法面临效率,存储,延迟和准确性的权衡.
  • 现有的解决方案难以满足边缘设备上现代人工智能应用的苛刻要求.

研究的目的:

  • 开发一种混合精度异质CIMAI边缘处理器,克服传统同质设计的局限性.
  • 为了使网络层在不同的芯片 CIM 架构和计算格式之间进行层级/内核级分区.
  • 在能源效率,存储,唤醒延迟和推断准确性方面实现同时优化.

主要方法:

  • 实现一个整合memristor-CIM,SRAM-CIM和数字单元的异质CIM架构.
  • 开发一个灵活的AI网络层分层/核心分层系统.
  • 基于错误灵敏度分析的混合精度计算 (整数和浮点) 的支持.

主要成果:

  • 实现了高能源效率: 40.91 TFLOPS/W (ResNet-20) 和 28.63 TFLOPS/W (移动网络-v2).
  • 在ResNet-20和MobileNet-v2中显示的低精度降解:<0.45%.
  • 达到了373.52微秒的快速唤醒响应时间.

结论:

  • 拟议的混合精度异质CIM处理器有效地平衡了AI边缘设备的能效,准确性和速度.
  • 层级/内核级分区为各种人工智能工作负载提供了强大的硬件级优化策略.
  • 这种方法为下一代人工智能边缘计算提供了具有成本效益的,准备好的解决方案.