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.inference.compensation package
  • View page source

aihwkit.inference.compensation package

Compensation methods such as drift compensation during analog inference.

Submodules

  • aihwkit.inference.compensation.base module
    • BaseDriftCompensation
      • BaseDriftCompensation.apply()
      • BaseDriftCompensation.get_readout_tensor()
      • BaseDriftCompensation.init_baseline()
      • BaseDriftCompensation.readout()
  • aihwkit.inference.compensation.drift module
    • GlobalDriftCompensation
      • GlobalDriftCompensation.get_readout_tensor()
      • GlobalDriftCompensation.readout()
    • GlobalDriftCompensationWithExactReference
      • GlobalDriftCompensationWithExactReference.init_baseline()
    • PerColumnDriftCompensation
      • PerColumnDriftCompensation.get_readout_tensor()
      • PerColumnDriftCompensation.readout()

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