IEEE Solid-State Circuits Magazine - Fall 2017 - 47

highlight various opportunities where
circuit designers can help to address
these challenges.

Introduction
Machine learning is needed to extract
meaningful, and ideally actionab-
le, information from the enormous
amount of data that is being generated
and collected every day. Given the
sheer volume of data, the high energy
cost of communication, and the often
limited communication bandwidth,
there is an increasing need to perform
the analysis locally near the sensor
rather than sending the raw data to
the cloud. Enabling machine learning
at the edge also addresses important
concerns related to privacy, latency,
and security. Accordingly, embedded
machine learning has shown to be
beneficial for many applications such
as those in the multimedia and medi-
cal fields.
For instance, computer vision is
a form of machine learning that
extracts information from images
and videos, which are arguably the
largest portion of big data as they
account for ove r 70 % of today 's
Internet traffic [1]. In many appli-
cations (e.g., measuring wait times

We will give a short overview of the key
concepts in machine learning, discuss its
challenges particularly in the embedded
space, and highlight various opportunities
where circuit designers can help to address
these challenges.
in stores and traffic patterns), it is
desirable to extract the meaning-
ful information from the video at
the image sensor rather than in the
cloud to reduce the communication
cost. For other applications such as
autonomous vehicles, drone naviga-
tion, and robotics, local processing is
desired since the latency and security
risk of relying on the cloud are too
high. However, video involves high-
dimensional data, which is compu-
tationally expensive to process; thus,
low-cost hardware to analyze video
is challenging yet critical to enabl-
ing these applications. While there
is a wide range of computer vision
tasks [2], in this article, we will focus
on image classification as a driv-
ing example, where the task is to
determine the class of the object in an
image (Figure 1).

Another important application is
speech recognition, which enables
seamless interaction with electronic
devices, such as smartphones. Speech
recognition is the first step before many
other tasks such as machine translation
and natural language processing. Low-
power hardware for speech recognition
is explored in [3] and [4].
In the medical field, there is a clinical
need to collect long-term data to help
detect/diagnose various diwseases
or monitor treatment. For instance,
the constant monitoring of electro-
cardiogram or electroencephalogram
signals can identify cardiovascular
diseases or detect the onset of a sei-
zure for epilepsy patients, respec-
tively. In many cases, these devices
are either wearable or implantable,
and thus the energy consumption
must be kept to a minimum. The

IEEE SOLID-STATE CIRCUITS MAGAZINE

FA L L 2 0 17

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Table of Contents for the Digital Edition of IEEE Solid-State Circuits Magazine - Fall 2017
