IEEE Technology and Society Magazine - June 2020 - 49

consumption, that percentage is
projected to grow beyond 2% in
the coming years [1].
We believe that tracking and
communicating the environmental
impact of ML should be a key part
of the research and development
process, and have developed a tool
for estimating the carbon impact
of this process, the Machine Learning Emissions Calculator (see Figure 1). We present the tool and
its importance in the present article, and explore related issues
and challenges.

Factors Involved in Estimating
ML Carbon Emissions
Neural networks are essentially
complicated computer architectures with thousands - sometimes
millions - of connections and
weights, and millions of parallel calculations that have to be carried
out both during the training of the
network (when it learns a task, for
instance classifying images into
classes) and during inference time
(when the result of the training is
applied on a new sample, i.e., an
image that was not seen during
training). In fact, training neural networks is a complicated balance of
parameter tuning, optimization,
and often much trial and error. For
each successful training of a network, which ends up having the
network successfully do the task
that it was meant to do, there are
dozens and even hundreds of failed
experiments. This means that
while, in itself, a single training procedure of a given neural network is
not necessarily very energy-consuming (and carbon-emitting), if all of
the experiments are taken into
account, this can quickly add up to
a significant amount of emissions.
There are a few factors that have
the biggest impact on these emissions, however, and we will discuss
these below.
JUNE 2020

FIGURE 1. Machine Learning Emissions Calculator: Web Interface.

Type of Energy Used
Few energy consumers have the ability to choose how much carbon they
will produce when plugging in a
device to a power outlet in their
home or office; in most cases, this is
defined by the energy source that the
outlet is connected to. In the case of
a physical device, this means that the
energy is taken from the local energy
grid of the socket's location, which
can be generated from coal, hydroelectricity, solar, nuclear, or any com∕

IEEE TECHNOLOGY AND SOCIETY MAGAZINE

bination of these (and others). These
sources can be broadly divided into
renewable energy, collected from
sources that are naturally replenished
relatively quickly (e.g., wind, solar,
hydro, tidal, etc.), and nonrenewable
energy sources, which can take millions of years to be formed again (this
is the case for coal).
In the case of Machine Learning
models trained locally on a server
connected to the power socket in
the nearest wall, not much can be

49



IEEE Technology and Society Magazine - June 2020

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