IBM Analog Hardware Acceleration Kit
v0.5.1

Get started

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

Analog AI Concepts

  • Analog AI
  • Analog AI Hardware
  • Advantages and Challenges

Using the Simulator

  • Using aihwkit Simulator
  • Using Experiments

Analog DNN Training

  • Specialized Update Algorithms
  • Analog Training Presets

Analog DNN Inference

  • Inference and PCM statistical model

Advanced Guides

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

References

  • API Reference
  • Paper References
IBM Analog Hardware Acceleration Kit
  • »
  • Paper References
  • Edit on GitHub

Paper ReferencesΒΆ

  • [1] 2020 Nature Nanotechnology, Memory devices and applications for in-memory computing

  • [2] 2020 Nature Communications. Accurate deep neural network inference using computational phase-change memory

  • [3] 2020 Frontiers in Neuroscience, Acceleration of deep neural network training with resistive cross-point devices: Design considerations

  • [4] 2020 Frontiers in Neuroscience, Mixed-precision deep learning based on computational memory

  • [5] 2018 Nature, Equivalent-accuracy accelerated neural-network training using analogue memory

  • [6] 2018 Nature Communications, Signal and noise extraction from analog memory elements for neuromorphic computing

  • [7] 2019 IEEE Symposium on VLSI Technologies, Capacitor-based Cross-point Array for Analog Neural Network with Record Symmetry and Linearity

  • [8] 2018 International Electron Devices Meeting (IEDM), ECRAM as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing

  • [9] 2016 Frontiers in Neuroscience, Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

  • [10] 2020 Frontiers in Neuroscience, Algorithm for Training Neural Networks on Resistive Device Arrays

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