Communications of the ACM

practice
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

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.