Future Computing Systems (FCS) to support 'understanding' capability

Abstract

© 2019 IEEE. The massive explosion in data acquisition, processing, and archiving, accelerated by the end of Moore's Law, creates a challenge and an opportunity for a complete redesign of technology, devices, hardware architecture, software stack and AI stack to enable future computing systems with 'understanding' capability. We propose a Future Computing System (FCS) based on a memory driven computing AI architecture, that leverages different types of next generation accelerators (e.g., Ising and Hopfield Machines), connected over an intelligent successor of the Gen-Z interconnect. On top of this architecture we propose a software stack and subsequently, an AI stack built on top of the software stack. While intelligence characteristics (learning, training, self-awareness, etc.) permeate all layers, we also separate AI-specific components into a separate layer for clear design. There are two aspects of AI in FCSs: a) AI embedded in the system to make the system better: better performing, more robust, self-healing, maintainable, repairable, and energy efficient. b) AI as the level of reasoning over the information contained within the system: the supervised and unsupervised techniques finding relationships over the data placed into the system. Developing the software and AI stack will require adapting to each redundant component. At least initially, specialization will be required. For this reason, starting with an interoperable, memory driven computing architecture and associated interconnect is essential for subsequent generalization. Our architecture is composable, i.e., it could be pursued in: a) its entirety, b) per-layer c) per component inside of the layer (e.g., only one of the accelerators, use cases, etc.); or d) exploring specific characteristics across the layers.