5,345 research outputs found
VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
The hardware implementation of deep neural networks (DNNs) has recently
received tremendous attention: many applications in fact require high-speed
operations that suit a hardware implementation. However, numerous elements and
complex interconnections are usually required, leading to a large area
occupation and copious power consumption. Stochastic computing has shown
promising results for low-power area-efficient hardware implementations, even
though existing stochastic algorithms require long streams that cause long
latencies. In this paper, we propose an integer form of stochastic computation
and introduce some elementary circuits. We then propose an efficient
implementation of a DNN based on integral stochastic computing. The proposed
architecture has been implemented on a Virtex7 FPGA, resulting in 45% and 62%
average reductions in area and latency compared to the best reported
architecture in literature. We also synthesize the circuits in a 65 nm CMOS
technology and we show that the proposed integral stochastic architecture
results in up to 21% reduction in energy consumption compared to the binary
radix implementation at the same misclassification rate. Due to fault-tolerant
nature of stochastic architectures, we also consider a quasi-synchronous
implementation which yields 33% reduction in energy consumption w.r.t. the
binary radix implementation without any compromise on performance.Comment: 11 pages, 12 figure
A self-adapting latency/power tradeoff model for replicated search engines
For many search settings, distributed/replicated search engines deploy a large number of machines to ensure efficient retrieval. This paper investigates how the power consumption of a replicated search engine can be automatically reduced when the system has low contention, without compromising its efficiency. We propose a novel self-adapting model to analyse the trade-off between latency and power consumption for distributed search engines. When query volumes are high and there is contention for the resources, the model automatically increases the necessary number of active machines in the system to maintain acceptable query response times. On the other hand, when the load of the system is low and the queries can be served easily, the model is able to reduce the number of active machines, leading to power savings. The model bases its decisions on examining the current and historical query loads of the search engine. Our proposal is formulated as a general dynamic decision problem, which can be quickly solved by dynamic programming in response to changing query loads. Thorough experiments are conducted to validate the usefulness of the proposed adaptive model using historical Web search traffic submitted to a commercial search engine. Our results show that our proposed self-adapting model can achieve an energy saving of 33% while only degrading mean query completion time by 10 ms compared to a baseline that provisions replicas based on a previous day's traffic
Efficient Task Replication for Fast Response Times in Parallel Computation
One typical use case of large-scale distributed computing in data centers is
to decompose a computation job into many independent tasks and run them in
parallel on different machines, sometimes known as the "embarrassingly
parallel" computation. For this type of computation, one challenge is that the
time to execute a task for each machine is inherently variable, and the overall
response time is constrained by the execution time of the slowest machine. To
address this issue, system designers introduce task replication, which sends
the same task to multiple machines, and obtains result from the machine that
finishes first. While task replication reduces response time, it usually
increases resource usage. In this work, we propose a theoretical framework to
analyze the trade-off between response time and resource usage. We show that,
while in general, there is a tension between response time and resource usage,
there exist scenarios where replicating tasks judiciously reduces completion
time and resource usage simultaneously. Given the execution time distribution
for machines, we investigate the conditions for a scheduling policy to achieve
optimal performance trade-off, and propose efficient algorithms to search for
optimal or near-optimal scheduling policies. Our analysis gives insights on
when and why replication helps, which can be used to guide scheduler design in
large-scale distributed computing systems.Comment: Extended version of the 2-page paper accepted to ACM SIGMETRICS 201
Learning Scheduling Algorithms for Data Processing Clusters
Efficiently scheduling data processing jobs on distributed compute clusters
requires complex algorithms. Current systems, however, use simple generalized
heuristics and ignore workload characteristics, since developing and tuning a
scheduling policy for each workload is infeasible. In this paper, we show that
modern machine learning techniques can generate highly-efficient policies
automatically. Decima uses reinforcement learning (RL) and neural networks to
learn workload-specific scheduling algorithms without any human instruction
beyond a high-level objective such as minimizing average job completion time.
Off-the-shelf RL techniques, however, cannot handle the complexity and scale of
the scheduling problem. To build Decima, we had to develop new representations
for jobs' dependency graphs, design scalable RL models, and invent RL training
methods for dealing with continuous stochastic job arrivals. Our prototype
integration with Spark on a 25-node cluster shows that Decima improves the
average job completion time over hand-tuned scheduling heuristics by at least
21%, achieving up to 2x improvement during periods of high cluster load
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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