Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. The AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, that provides the benefits of using the AIHWKit simulation platform in a fully managed cloud setting. Finally, we show examples on how users can expand and customize AIHWKit for their own needs. This tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial.

IBM's new AI Hardware Composer tool aims to boost the performance of analog AI hardware. The tool is being released on the second anniversary of the IBM Research AI Hardware Center. IBM's pioneering centre launched in 2019 with the aim of improving AI hardware compute efficiency by 2.5 times every year for a decade. AI Hardware Composer claims to help both novice and experienced developers to create neural networks and tune analog devices to build accurate AI models. The new tool can be used with IBM's existing Analog Hardware Acceleration Kit (AIHWKIT), an open-source Python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence.