You are one of the early users of the IBM Analog Hardware Acceleration Kit. The initial releases have been focused on releasing a basic PyTorch integration for exploring selected features of the analog devices simulator, and set the basis that will be extended and improved upon:
integration of more simulator features in the PyTorch interface
tools to improve inference accuracy by converting pre-trained models with hardware-aware training
algorithmic tools to improve training accuracy by compensating for material short-comings
additional analog neural network layers
additional analog optimizers
custom network architectures and dataset/model zoos
integration with the cloud
This document will be updated with more details as the roadmap for the project evolves. As a companion, please refer to the Issues tab in the repository for more in-depth details about the status of the implementation of the different features and a sneak peek into the next release.
We have an ambitious plan to incrementally bring new simulation and hardware
features to our users, but we are eager to hear your feedback on the features
of value for your work. Please contact us at
firstname.lastname@example.org for any
feedback or information.