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 this https URL
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 this https URL

将深度神经网络（DNN）适应并应用于模拟进存内计算（AIMC）芯片以达到数字计算相似精确度的过程可以通过 IBM 模拟硬件加速套件（AIHWKit）进行实现和评估。AIHWKit 是一个模拟使用 AIMC 进行 DNN 推理和训练的 Python 库，本教程介绍了 AIHWKit 的设计、功能以及正确进行推理和训练的最佳实践，同时也提供了一个完全托管的云环境 Analog AI Cloud Composer 来使用 AIHWKit 模拟平台的优势，并展示用户如何扩展和自定义 AIHWKit。