2 research outputs found
Intelligent Architectures for Intelligent Machines
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.Comment: To appear in VLSI DAT/TSA 2020 conference proceedings as a plenary
keynote pape
A Survey of Machine Learning Applied to Computer Architecture Design
Machine learning has enabled significant benefits in diverse fields, but,
with a few exceptions, has had limited impact on computer architecture. Recent
work, however, has explored broader applicability for design, optimization, and
simulation. Notably, machine learning based strategies often surpass prior
state-of-the-art analytical, heuristic, and human-expert approaches. This paper
reviews machine learning applied system-wide to simulation and run-time
optimization, and in many individual components, including memory systems,
branch predictors, networks-on-chip, and GPUs. The paper further analyzes
current practice to highlight useful design strategies and identify areas for
future work, based on optimized implementation strategies, opportune extensions
to existing work, and ambitious long term possibilities. Taken together, these
strategies and techniques present a promising future for increasingly automated
architectural design