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Memristive GAN in Analog.

O Krestinskaya1, B Choubey2, A P James3

  • 1Unaffiliated, Nur-Sultan, Kazakhstan.

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This summary is machine-generated.

This study introduces an Analog Memristive Deep Convolutional Generative Adversarial Network (AM-DCGAN) to accelerate computations on edge devices. This memristor-based approach significantly reduces power consumption for deep learning tasks.

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence Hardware
  • Solid-State Electronics

Background:

  • Generative Adversarial Networks (GANs) demand substantial computational resources, hindering their deployment on resource-constrained edge devices.
  • Conventional microprocessor-based implementations of GANs are often slow and energy-intensive.
  • Memristive devices offer a promising avenue for energy-efficient, in-memory computing.

Purpose of the Study:

  • To propose and simulate an Analog Memristive Deep Convolutional GAN (AM-DCGAN) for accelerating neural computations.
  • To demonstrate the feasibility of implementing GANs on edge devices using analog memristive neural networks.
  • To analyze the power efficiency and performance of the proposed memristor-based GAN architecture.

Main Methods:

  • Designed an AM-DCGAN utilizing memristive neural networks for both generator (deconvolutional) and discriminator (convolutional) components.
  • Simulated the system at the circuit level, incorporating 1.7 million memristor devices and accounting for non-idealities.
  • Employed neural network dropout principles for regularization and power reduction.
  • Conducted SPICE level simulations using 0.18 μm CMOS technology and WOₓ memristive devices.

Main Results:

  • Achieved a minimum average power consumption of 47nW per neural computation.
  • The modular design features crossbar arrays for efficient memristor integration.
  • Demonstrated effective regularization and power reduction through dropout principles.
  • Simulations confirmed the viability of the AM-DCGAN architecture with specified memristor characteristics (R_ON=40 kΩ, R_OFF=250 kΩ, V_th=0.8 V, V_write=1.0 V).

Conclusions:

  • The proposed AM-DCGAN effectively accelerates intensive neural computations required by GANs.
  • Memristive neural networks in the analog domain offer a power-efficient solution for edge device implementation of GANs.
  • The design demonstrates a significant reduction in power consumption, making advanced AI feasible on low-power hardware.