IBM Analog Hardware Acceleration Kit

Get started

  • Installation
  • Advanced installation guide
  • Using the PyTorch integration
  • Glossary

Analog AI Concepts

  • Analog AI
  • Analog AI Hardware
  • Advantages and Challenges

Cloud/Composer

  • Analog AI Cloud Composer Overview
  • Composer CLI

Using the Simulator

  • Using aihwkit Simulator

Analog DNN Training

  • Specialized Update Algorithms
  • Analog Training Presets

Analog DNN Inference

  • Inference and PCM Statistical Model
  • Analog Hardware-aware Training
  • Inference with Analog CMO-ReRAM Statistical Model

Advanced Guides

  • aihwkit design
  • Development setup
  • Development conventions
  • Project roadmap
  • Changelog

References

  • API Reference
  • Paper References
IBM Analog Hardware Acceleration Kit
  • aihwkit.optim package
  • View page source

aihwkit.optim package

Analog Optimizers.

Submodules

  • aihwkit.optim.analog_optimizer module
    • AnalogAdam
    • AnalogOptimizer
      • AnalogOptimizer.SUBCLASSES
    • AnalogOptimizerMixin
      • AnalogOptimizerMixin.regroup_param_groups()
      • AnalogOptimizerMixin.set_learning_rate()
      • AnalogOptimizerMixin.step()
    • AnalogSGD
  • aihwkit.optim.context module
    • AnalogContext
      • AnalogContext.cpu()
      • AnalogContext.cuda()
      • AnalogContext.get_data()
      • AnalogContext.has_gradient()
      • AnalogContext.reset()
      • AnalogContext.set_data()
      • AnalogContext.set_indexed()
      • AnalogContext.to()

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