Intelligent Architectures for Intelligent Machines

TL;DR

This paper advocates for data-centric, data-driven, and data-aware intelligent architectures to overcome data bottlenecks in modern computing, proposing novel in-memory and 3D-stacked memory techniques for improved efficiency and performance.

Contribution

It introduces a framework emphasizing data-centric principles and discusses innovative in-memory and 3D memory solutions to enhance computing efficiency and scalability.

Findings

Memory-based bulk operations exploiting analog properties

Using 3D-stacked memory logic layers for acceleration

Reducing memory latency and energy consumption

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. As a result, many key applications' performance, efficiency and scalability are bottlenecked by data movement. In this keynote talk, we describe three major shortcomings of modern architectures in terms of 1) dealing with data, 2) taking advantage of the vast amounts of data, and 3) exploiting different semantic properties of application data. 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) data-aware. We give several examples for how to exploit each of these principles to design a much more efficient and high performance computing system. We especially discuss recent research that aims to fundamentally reduce memory latency and energy, and practically enable computation close to data, with at least two promising novel directions: 1) performing massively-parallel bulk operations in memory by exploiting the analog operational properties of memory, with low-cost changes, 2) exploiting the logic layer in 3D-stacked memory technology in various ways to accelerate important data-intensive applications. 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.