OUR FOURTH INSTALLMENT of Research for Practice covers
two of the hottest topics in computer science research
and practice: cryptocurrencies and deep learning.
First, Arvind Narayanan and Andrew Miller, co-
authors of the increasingly popular open access Bitcoin
textbook, provide an overview of ongoing research in
cryptocurrencies. This is a topic with a long history
in the academic literature that has recently come to
prominence with the rise of Bitcoin, blockchains, and
similar implementations of advanced, decentralized
protocols. These developments—and colorful exploits
such as the DAO vulnerability in June
2016—have captured the public imagi-
nation and the eye of the popular press.
In the meantime, academics have been
busy, delivering new results in main-
taining anonymity, ensuring usability,
detecting errors, and reasoning about
decentralized markets, all through the
lens of these modern cryptocurrency
systems. It is a pleasure having two
academic experts deliver the latest up-
dates from the burgeoning body of aca-
demic research on this subject.
Next, Song Han provides an overview of hardware trends related to another long-studied academic problem
that has recently seen an explosion in
popularity: deep learning. Fueled by
large amounts of training data and inexpensive parallel and scale-out compute, deep-learning-model architectures have seen a massive resurgence
of interest based on their excellent
performance on traditionally difficult tasks such as image recognition.
These deep networks are compute-intensive to train and evaluate, and
many of the best minds in computer
systems (for example, the team that
developed MapReduce) and AI are
working to improve them. As a result,
Song has provided a fantastic overview of recent advances devoted to
using hardware and hardware-aware
techniques to compress networks,
improve their performance, and reduce their often large amounts of energy consumption.
As always, our goal in this column is
to allow our readers to become experts
in the latest topics in computer science
research in a weekend afternoon’s
worth of reading. To facilitate this
process, as always, we have provided
open access to the ACM Digital Library
for the relevant citations from these
selections so you can read the research
results in full. Please enjoy!
Peter Bailis is an assistant professor of computer science
at Stanford University. His research in the Future Data
Systems group ( futuredata.stanford.edu/) focuses on
the design and implementation of next-generation data-intensive systems.
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