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.nn.low_precision_modules package
  • View page source

aihwkit.nn.low_precision_modules package

Submodules

  • aihwkit.nn.low_precision_modules.conversion_utils module
    • append_default_conversions()
    • get_module_args()
  • aihwkit.nn.low_precision_modules.quantization_states module
    • enable_quant_states()
    • estimate_act_ranges()
    • estimate_ranges()
    • estimate_ranges_train()
    • fix_act_ranges()
    • fix_ranges()
    • fix_weight_ranges()
    • full_precision()
    • full_precision_acts()
    • full_precision_weights()
    • learn_ranges()
    • quantized()
    • quantized_acts()
    • quantized_weights()
    • reset_act_ranges()
    • set_quant_state()
  • aihwkit.nn.low_precision_modules.quantized_base_modules module
    • QuantBatchNorm2d
      • QuantBatchNorm2d.forward()
    • QuantConv2d
      • QuantConv2d.run_forward()
    • QuantEmbedding
      • QuantEmbedding.run_forward()
    • QuantLayerNorm
      • QuantLayerNorm.run_forward()
    • QuantLinear
      • QuantLinear.run_forward()
  • aihwkit.nn.low_precision_modules.quantized_input_module module
    • QuantizedInputModule
      • QuantizedInputModule.forward()

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