IEEE Solid-States Circuits Magazine - Fall 2022 - 18
Marian Verhelst , Man Shi , and Linyan Mei
ML Processors Are
Going Multi-Core
A performance
dream or a
scheduling
nightmare?
A
18
FALL 2022
pplications of
machine learning
(ML) increasingly
penetrate into our
daily routines, our
work, and our living environments.
In this way, more complex machine
intelligence algorithms fundamentally
change and enhance the way we
live, work, and interact. However, the
real-time deployment of these algorithms
toward the dreams of smart
Digital Object Identifier 10.1109/MSSC.2022.3201783
Date of current version: 11 November 2022
spaces, digital twins, the metaverse,
or personalized health care requires
a powerful compute continuum from
cloud to (extreme) edge, capable of
efficiently executing the computehungry
ML workloads, such as deep
neural networks (NNs). To enable the
required real-time responsiveness at
affordable energy or power budgets,
many ML-optimized custom processors
(also called accelerators) have
been presented over the past decade,
as depicted in Figure 1.
When reflecting upon this young
and vibrant field, a clear and interestIEEE
SOLID-STATE CIRCUITS MAGAZINE
ing trend starts to appear. In their quest
for higher and higher throughput, ML
accelerators have evolved from small
single-core designs, over scaled up
datapath arrays, to homogeneous and
heterogeneous multi-core implementations.
While the trend toward multi-core
processors without any doubt brings
large performance gains, they come at
the expense of significant implementation
and scheduling challenges. In this
article, we will dig deeper into the underlying
motivations of this multi-core
evolution, the accompanying challenges,
and exciting future opportunities.
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IEEE Solid-States Circuits Magazine - Fall 2022
Table of Contents for the Digital Edition of IEEE Solid-States Circuits Magazine - Fall 2022
Contents
IEEE Solid-States Circuits Magazine - Fall 2022 - Cover1
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IEEE Solid-States Circuits Magazine - Fall 2022 - Contents
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