We want to invite you to submit your latest research to the MDPI
Sustainability Journal, Special Issue on “Energy-Efficient Computing
Systems for Deep Learning” which is open for submissions until April 30,
2021.

https://www.mdpi.com/journal/sustainability/special_issues/Energy-Efficient_Computing

Deep learning (DL) is receiving much attention these days due to the
impressive performance achieved in a variety of application areas, such
as computer vision, natural language processing, machine translation,
and many more. Aimed at achieving ever-faster processing of these DL
workloads in an energy-efficient way, a myriad of specialized hardware
architectures (e.g., sparse tensor cores in NVIDIA A100 GPU) and
accelerators (e.g., Google TPU) are emerging. The goal is to provide
much higher performance-per-watt than general-purpose CPU processors.
Production deployments tend to have very high model complexity and
diversity, demanding solutions that can deliver higher productivity,
more powerful programming abstractions, more efficient software and
system architectures, faster runtime systems, and numerical libraries,
accompanied by a rich set of analysis tools.

DL models are generally memory and computationally intensive, for both
training and inference. Accelerating these operations in an
energy-efficient way has obvious advantages, first by reducing energy
consumption (e.g., data centers can consume megawatts, producing an
electricity bill similar to that of a small town), and secondly, by
making these models usable on smaller battery-operated devices at the
edge of the Internet. Edge devices run on strict power budgets and
highly constrained computing power. In addition, while deep neural
networks have motivated much of this effort, numerous applications and
models involve a wider variety of operations, network architectures, and
data processing. These applications and models are a challenge for
today’s computer architectures, system stacks, and programming
abstractions. As a result, non-von Neumann computing systems such as
those based on in-memory and/or in-network computing, which perform
specific computational tasks just where the data are generated, are
being investigated in order to avoid the latency of shuttling huge
amounts of data back and forth between processing and memory units.
Additionally, machine learning (ML) techniques are being explored to
reduce overall energy consumption in computing systems. These
applications of ML range from energy-aware scheduling algorithms in data
centers to battery life prediction techniques in edge devices. The high
level of interest in these areas calls for a dedicated journal issue to
discuss novel acceleration techniques and computation paradigms for
energy-efficient DL algorithms. Since the journal targets the
interaction of machine learning and computing systems, it will
complement other publications specifically focused on one of these two
parts in isolation.

The main objective of this Special Issue is to discuss and disseminate
the current work in this area, showcasing new and novel DL algorithms,
programming paradigms, software tools/libraries, and hardware
architectures oriented at providing energy efficiency, in particular
(but not limited to):

– Novel energy-efficient DL systems: heterogeneous multi/many-core
systems, GPUs, and FPGAs;
– Novel energy-efficient DL hardware accelerators and associated software;
– Emerging semiconductor technologies with applications to
energy-efficient DL hardware acceleration;
– Cloud and edge energy-efficient DL computing: hardware and software to
accelerate training and inference;
– In-memory computation and in-network computation for energy-efficient
DL processing;
– Machine-learning-based techniques for managing energy efficiency of
computing platforms.

Dr. José Cano
Dr. José L. Abellán
Prof. David Kaeli
Guest Editors