Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference

Abstract

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, a platform that provides the benefits of using the AIHWKit simulation in a fully managed cloud setting along with physical AIMC hardware access, freely available at https://aihw-composer.draco.res.ibm.com. 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.

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
• A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays
• Mixed-precision deep learning based on computational memory
• MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems
• AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing

Cited by in corpus (6)

• The Inherent Adversarial Robustness of Analog In-Memory Computing
• Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training
• In-Materia Speech Recognition
• Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators
• A Scalable FPGA Architecture With Adaptive Memory Utilization for GEMM-Based Operations
• Efficient transformer adaptation for analog in-memory computing via low-rank adapters