Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference (2307.09357v2)

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.

• The paper introduces the IBM AIHWKIT, demonstrating its ability to simulate analog in-memory operations to meet the growing computational demands of DNNs.
• The paper details methodologies like in-memory SGD, Tiki-Taka, and mixed-precision training to mitigate noise and device variability during both training and inference.
• The paper emphasizes the toolkit’s modular design and the cloud-based Analog AI Cloud Composer, which enhances accessibility and supports customizable hardware-aware neural network experiments.

Overview of the AIHWKit for Neural Network Training and Inference

The paper, titled "Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference," presents a comprehensive exploration of the IBM AI Hardware Acceleration Kit (AIHWKIT), a tool designed for simulating the training and inference of Deep Neural Networks (DNNs) on Analog In-Memory Computing (AIMC) systems.

The motivation for this work arises from the increasing computational demands associated with DNNs, coupled with the diminishing returns of conventional scaling laws like Moore’s law. AIMC offers a promising solution to these issues by enabling efficient computation directly within memory cells, significantly reducing latency and energy consumption.

Content Breakdown

AIMC Concepts and AIHWKIT Design:

The paper begins with an in-depth explanation of AIMC principles, focusing on how memory devices, such as Phase Change Memory (PCM), Resistive Random Access Memory (RRAM), and others, perform matrix-vector multiplications (MVM) directly in the memory arrays. These operations are critical for AIMC's potential to accelerate neural network computations.

The authors describe the AIHWKIT's architecture, which integrates analog layers within conventional neural network frameworks like PyTorch. This integration enables seamless simulation of mixed analog and digital components within neural networks. The toolkit distinguishes between inference and on-chip training, employing various strategies and configurations for each.

Training and Inference:

For DNN training, AIHWKIT supports multiple approaches, including in-memory Stochastic Gradient Descent (SGD) and more advanced algorithms like Tiki-Taka (TTv2) and Mixed-Precision (MP) training. These methods exploit AIMC's speed and efficiency while addressing inherent device variability and noise.

In the context of inference, the toolkit incorporates nonidealities due to device noise and drift, providing robust simulation frameworks that account for variability during model evaluation. This involves techniques like hardware-aware training (HWA), which modulates training to accommodate expected hardware deviations.

Extensibility and Customization:

AIHWKIT's modular design allows for significant customization, from defining new noise models for inference to implementing custom MVM nonidealities. This flexibility supports a wide array of research possibilities, from exploring novel device technologies to developing improved training algorithms.

Analog AI Cloud Composer:

The cloud-based Analog AI Cloud Composer (AAICC) offers an interactive platform for using AIHWKIT, facilitating analog neural network experiments without the need for in-depth coding. This platform extends AIHWKIT's accessibility for researchers by offering templated workflows and direct hardware access, notably including IBM's PCM-based fusion chip.

Implications and Future Directions

The paper underscores the potential of AIHWKIT to significantly impact the development of energy-efficient AI hardware by enabling detailed exploration of AIMC systems. It prompts further research into optimizing both analog devices and their integration into AI models, thereby potentially redefining the landscape of AI acceleration technologies.

Future developments could include enhanced compatibility with existing AI training pipelines, incorporation of performance estimations for power and latency, and expansion of the toolkit's capabilities to support a broader array of applications and hardware configurations.

In summary, this work presents a critical resource for researchers aiming to bridge the gap between emerging analog computational paradigms and mainstream AI applications. The AIHWKIT, with its open-source availability, invites collaborative innovation, fostering advancements in the realization of sustainable and high-performance AI systems.