Public Lecture #4

"Intelligent Architectures for Intelligent Systems”

Prof. Onur MUTLU (ETH)

Abstract

Computing is bottlenecked by data. Large amounts of 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. 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 system 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 will 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 computation 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.

Reference papers:

1. "Intelligent Architectures for Intelligent Computing Systems"  
2. "A Modern Primer on Processing in Memory"   

Reference video:

1. IEDM 2020 Tutorial: Memory-Centric Computing Systems, Onur Mutlu, 12 December 2020  

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