Analog AI as a Service: A Cloud Platform for In-Memory Computing

Kaoutar El Maghraoui, Kim Tran, Kurtis Ruby, Borja Godoy, Jordan Murray, Manuel Le Gallo-Bourdeau, Todd Deshane, Pablo Gonzalez, Diego Moreda, Hadjer Benmeziane, Corey Liam Lammie, Julian Büchel, Malte J. Rasch, Abu Sebastian, Vijay Narayanan

Abstract: This paper introduces the Analog AI Cloud Composer platform, a service that allows users to access Analog In-Memory Computing (AIMC) simulation and computing resources over the cloud. We introduce the concept of an Analog AI as a Service (AAaaS). AIMC offers a novel approach for decreasing both the latency and energy usage associated with Deep Neural Network (DNN) inference and training. This platform democratizes access to AIMC computing, making it available to a broader audience, including researchers, developers, and businesses. Emphasizing a user-friendly, no-code approach, AAaaS integrates the Analog Hardware Acceleration Kit (AIHWKit) simulation platform within a fully managed cloud environment. We discuss the architecture of the Analog AI Cloud Composer (AAICC), focusing on its key services such as inference, training, and AIMC hardware access. The platform's design, grounded in cloud services and guidelines, ensures a secure, data-centric user experience with robust control and validation mechanisms.