Intelligent Architectures for Intelligent Computing Systems

TL;DR

This paper advocates for data-centric, data-driven, and data-aware intelligent architectures to address data bottlenecks in modern computing, proposing principles for more efficient, high-performance, and sustainable systems.

Contribution

It introduces a novel framework emphasizing three key principles for designing intelligent architectures tailored to handle data effectively.

Findings

Examples demonstrating the application of the principles

Discussion on enabling adoption of intelligent architectures

Guiding principles for future system designs

Abstract

Computing is bottlenecked by data. Large amounts of application data overwhelm storage capability, communication capability, and computation capability of the modern machines we design today. We argue that an intelligent architecture should be designed to handle data well. We show that handling data well requires designing architectures based on three key principles: 1) data-centric, 2) data-driven, 3) dataaware. We give several examples for how to exploit each of these principles to design a much more efficient and high performance computing system. We discuss how to enable adoption of such fundamentally more intelligent architectures, which we believe are key to efficiency, performance, and sustainability. We conclude with some guiding principles for future computing architecture and system designs. This accompanying short paper provides a summary of the associated invited talk at DATE 2021 and points the reader to further work that may be beneficial to examine.