  We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a
kind open source toolkit to simulate analog crossbar arrays in a convenient
fashion from within PyTorch (freely available at
https://github.com/IBM/aihwkit). The toolkit is under active development and is
centered around the concept of an "analog tile" which captures the computations
performed on a crossbar array. Analog tiles are building blocks that can be
used to extend existing network modules with analog components and compose
arbitrary artificial neural networks (ANNs) using the flexibility of the
PyTorch framework. Analog tiles can be conveniently configured to emulate a
plethora of different analog hardware characteristics and their non-idealities,
such as device-to-device and cycle-to-cycle variations, resistive device
response curves, and weight and output noise. Additionally, the toolkit makes
it possible to design custom unit cell configurations and to use advanced
analog optimization algorithms such as Tiki-Taka. Moreover, the backward and
update behavior can be set to "ideal" to enable hardware-aware training
features for chips that target inference acceleration only. To evaluate the
inference accuracy of such chips over time, we provide statistical programming
noise and drift models calibrated on phase-change memory hardware. Our new
toolkit is fully GPU accelerated and can be used to conveniently estimate the
impact of material properties and non-idealities of future analog technology on
the accuracy for arbitrary ANNs.
