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An ultrafast bipolar flash memory for self-activated in-memory computing.

Xiaohe Huang1, Chunsen Liu2,3, Zhaowu Tang1

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Summary
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Researchers developed ultrafast bipolar flash memory for self-activated multiply-accumulate (MAC) operations, significantly boosting in-memory computing energy efficiency. This innovation integrates activation functions directly into MAC, reducing power consumption from milliwatts to nanowatts.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Electrical Engineering

Background:

  • In-memory computing offers potential energy efficiency gains by performing multiply-accumulate (MAC) operations within memory arrays.
  • Current in-memory computing approaches struggle with power-intensive nonlinear activation functions, limiting overall efficiency.
  • Conventional methods separate MAC and activation, hindering performance.

Purpose of the Study:

  • To develop an in-memory computing solution that integrates MAC operations with nonlinear activation.
  • To enhance the energy efficiency of in-memory computing by reducing the power consumption of activation processes.
  • To achieve high accuracy in neural network computations using a novel self-activated MAC approach.

Main Methods:

  • Development of an ultrafast bipolar flash memory cell based on van der Waals heterostructures.
  • Implementation of self-activated MAC operations by utilizing sign matching between input voltage and storage charge.
  • Integration of a rectified linear unit (ReLU) activation function directly within the MAC process.
  • Simulation of a convolutional neural network (CNN) using the Modified National Institute of Standards and Technology (MNIST) dataset.

Main Results:

  • The developed bipolar flash cell exhibits ultrafast programming speeds (20-30 ns) and high endurance (8x10^6 cycles).
  • Self-activated MAC operations achieved extremely low power consumption (30 nW per operation, equivalent to 5 fJ).
  • Simulated CNN accuracy reached 97.23% on the MNIST dataset, comparable to conventional methods.

Conclusions:

  • The ultrafast bipolar flash memory enables efficient, self-activated MAC operations, overcoming limitations of traditional in-memory computing.
  • This approach significantly reduces energy consumption for activation functions, paving the way for more power-efficient AI hardware.
  • The demonstrated high accuracy suggests the viability of integrated MAC and activation for practical neural network applications.