Disciplined Approximate Computing: From Language to Hardware

Energy is increasingly a first-order concern in computer systems. Exploiting
energy-accuracy trade-offs is an attractive choice in applications that can
tolerate inaccuracies. Recent work has explored exposing this trade-off in
programming models. A key challenge, though, is how to isolate parts of the
program that must be precise from those that can be approximated so that a
program functions correctly even as quality of service degrades.

In this talk I will described our effort on co-designing language and hardware
to take advantage of approximate computing for significant energy savings. We
use type qualifiers to declare data that may be subject to approximate
computation. Using these types, the system automatically maps approximate
variables to low-power storage, uses low-power operations, and even applies
more energy-efficient algorithms provided by the programmer. In addition, the
system can statically guarantee isolation of the precise program component
from the approximate component. This allows a programmer to control explicitly
how information flows from approximate data to precise data. Importantly,
employing static analysis eliminates the need for dynamic checks, further
improving energy savings. I will describe a micro-architecture that offers
explicit approximate storage and computation and our recent proposal on using
neural networks as approximate accelerators for imperative programs. I will
conclude with an overview of our current/future research directions, including
language extensions quality-of-result specification, programming tools,
approximate storage and approximate wireless communication.

Luis Ceze is an Associate Professor in the Computer Science and
Engineering Department at the University of Washington. His research
focuses on computer architecture, programming languages and OS to
improve the programmability, reliability and energy efficiency of
multiprocessor systems. He has co-authored over 60 papers in these
areas, and had several papers selected as IEEE Micro Top Picks and
CACM research Highlights. He participated in the Blue Gene, Cyclops,
and PERCS projects at IBM and is a recipient of IBM awards. He is also
a recipient of an NSF CAREER Award, a Sloan Research Fellowship and a
Microsoft Research Faculty Fellowship. He consults for Microsoft
Research and co-founded Corensic, a UW-CSE spin-off company. He was
born and raised in Sao Paulo, Brazil, where it drizzles all the time;
he now lives in the similarly drizzly Seattle. When he is not working
he is found either eating or cooking.