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相关概念视频

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Design Example: Capacitance Multiplier Circuit01:20

Design Example: Capacitance Multiplier Circuit

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In integrated circuit technology, a capacitance multiplier is often utilized to produce a larger capacitance value when a small physical capacitance falls short. This is achieved by a circuit that multiplies capacitance values by a factor of up to 1000, such that a 10-pF capacitor can replicate the performance of a 100-nF capacitor.
The circuit illustrated in Figure 1 below incorporates two op-amps, with the first operating as a voltage follower and the second acting as an inverting amplifier.
783
Cascaded Op Amps01:16

Cascaded Op Amps

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Operational amplifiers (op-amps) are versatile electronic components that can be interconnected in a cascade - one after another in a linear sequence. This cascading is possible due to their infinite input resistance and zero output resistance, allowing them to maintain their input-output relationships even when connected in series.
In a cascaded system, each op-amp is referred to as a stage. The output of one stage drives the input of the subsequent stage. As the input signal passes through...
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Operational Amplifiers01:17

Operational Amplifiers

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The operational amplifier, often referred to as an op-amp, is a multifaceted building block of a circuit. This electronic component functions like a voltage-controlled voltage source and can also be used to create a voltage- or current-controlled current source. The design of an operational amplifier enables it to execute mathematical operations when external components like resistors and capacitors are linked to its terminals. An op-amp has the capacity to sum signals, amplify a signal,...
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Inverting and Non-inverting OpAmps01:20

Inverting and Non-inverting OpAmps

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In an inverting amplifier, the input voltage is connected through a resistor to the inverting terminal. Meanwhile, the non-inverting terminal is grounded and a feedback resistor is established between the inverting and output terminal, as depicted in Figure 1.
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Network Function of a Circuit01:25

Network Function of a Circuit

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Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
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相关实验视频

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对于低功率混合信号CNN处理芯片的模拟卷积操作器电路.

Malik Summair Asghar1,2, Saad Arslan3, HyungWon Kim1

  • 1Department of Electronics, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括

本研究引入了一种用于卷积神经网络 (CNN) 芯片的新型混合信号方法,可显著降低多重积累 (MAC) 单元的功率和面积消耗,以实现高效的AI处理.

关键词:
模拟乘法器模拟乘法器人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.混合信号的卷积操作.神经网络加速器神经网络加速器神经形态工程的神经形态工程

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科学领域:

  • 电气工程 电气工程
  • 计算机工程 计算机工程
  • 人工智能 硬件 硬件

背景情况:

  • 卷积神经网络 (CNN) 对人工智能至关重要,但它们的处理芯片消耗大量的电力和区域,主要是由于卷积运算符.
  • 多倍积累 (MAC) 单元是CNN计算的核心,推动了对高效硬件实现的需求.

研究的目的:

  • 为在CNN处理芯片中实施卷积运算符提出一个紧且低功耗的混合信号方法.
  • 为了减少CNN硬件中与MAC单元相关的芯片面积和功耗.

主要方法:

  • 开发了一种混合信号卷积运算符,使用低功耗的二元加权电流转向数字对模拟转换器 (DAC) 和积累电容器.
  • 集成了一种用于处理负MAC结果的新型电荷共享技术和一个模拟最大共享电路.
  • 实现了一个CNN处理芯片与模拟卷积运算符,MAC电路,并使用55nmCMOS工艺模拟最大聚合单元.

主要成果:

  • 拟议的混合信号CNN芯片实现了0.0559mm2的面积,并消耗了540.6μW.
  • 与传统的数字CNN芯片相比,显著减少了84.21%的面积和91.85%的能量.
  • 由于其对称的设计,模拟卷积运算机提供了更好的精度,更小的面积和更低的功耗.

结论:

  • 拟议的混合信号卷积运算符为CNN中的数字实现提供了可行的,低功耗和面积高效的替代方案.
  • 这种方法可以广泛适应各种CNN模型,为更高效的AI硬件铺平道路.
  • 开发的模拟MAC单元和电路代表了节能AI处理的重大进步.