Intelligent Architectures for Intelligent Machines
Onur Mutlu

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…
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