6,786 research outputs found
Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques
The rapid growth of demanding applications in domains applying multimedia
processing and machine learning has marked a new era for edge and cloud
computing. These applications involve massive data and compute-intensive tasks,
and thus, typical computing paradigms in embedded systems and data centers are
stressed to meet the worldwide demand for high performance. Concurrently, the
landscape of the semiconductor field in the last 15 years has constituted power
as a first-class design concern. As a result, the community of computing
systems is forced to find alternative design approaches to facilitate
high-performance and/or power-efficient computing. Among the examined
solutions, Approximate Computing has attracted an ever-increasing interest,
with research works applying approximations across the entire traditional
computing stack, i.e., at software, hardware, and architectural levels. Over
the last decade, there is a plethora of approximation techniques in software
(programs, frameworks, compilers, runtimes, languages), hardware (circuits,
accelerators), and architectures (processors, memories). The current article is
Part I of our comprehensive survey on Approximate Computing, and it reviews its
motivation, terminology and principles, as well it classifies and presents the
technical details of the state-of-the-art software and hardware approximation
techniques.Comment: Under Review at ACM Computing Survey
Number Systems for Deep Neural Network Architectures: A Survey
Deep neural networks (DNNs) have become an enabling component for a myriad of
artificial intelligence applications. DNNs have shown sometimes superior
performance, even compared to humans, in cases such as self-driving, health
applications, etc. Because of their computational complexity, deploying DNNs in
resource-constrained devices still faces many challenges related to computing
complexity, energy efficiency, latency, and cost. To this end, several research
directions are being pursued by both academia and industry to accelerate and
efficiently implement DNNs. One important direction is determining the
appropriate data representation for the massive amount of data involved in DNN
processing. Using conventional number systems has been found to be sub-optimal
for DNNs. Alternatively, a great body of research focuses on exploring suitable
number systems. This article aims to provide a comprehensive survey and
discussion about alternative number systems for more efficient representations
of DNN data. Various number systems (conventional/unconventional) exploited for
DNNs are discussed. The impact of these number systems on the performance and
hardware design of DNNs is considered. In addition, this paper highlights the
challenges associated with each number system and various solutions that are
proposed for addressing them. The reader will be able to understand the
importance of an efficient number system for DNN, learn about the widely used
number systems for DNN, understand the trade-offs between various number
systems, and consider various design aspects that affect the impact of number
systems on DNN performance. In addition, the recent trends and related research
opportunities will be highlightedComment: 28 page
Layered architecture for quantum computing
We develop a layered quantum computer architecture, which is a systematic
framework for tackling the individual challenges of developing a quantum
computer while constructing a cohesive device design. We discuss many of the
prominent techniques for implementing circuit-model quantum computing and
introduce several new methods, with an emphasis on employing surface code
quantum error correction. In doing so, we propose a new quantum computer
architecture based on optical control of quantum dots. The timescales of
physical hardware operations and logical, error-corrected quantum gates differ
by several orders of magnitude. By dividing functionality into layers, we can
design and analyze subsystems independently, demonstrating the value of our
layered architectural approach. Using this concrete hardware platform, we
provide resource analysis for executing fault-tolerant quantum algorithms for
integer factoring and quantum simulation, finding that the quantum dot
architecture we study could solve such problems on the timescale of days.Comment: 27 pages, 20 figure
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