4,768 research outputs found
Learning Algorithms for Minimizing Queue Length Regret
We consider a system consisting of a single transmitter/receiver pair and
channels over which they may communicate. Packets randomly arrive to the
transmitter's queue and wait to be successfully sent to the receiver. The
transmitter may attempt a frame transmission on one channel at a time, where
each frame includes a packet if one is in the queue. For each channel, an
attempted transmission is successful with an unknown probability. The
transmitter's objective is to quickly identify the best channel to minimize the
number of packets in the queue over time slots. To analyze system
performance, we introduce queue length regret, which is the expected difference
between the total queue length of a learning policy and a controller that knows
the rates, a priori. One approach to designing a transmission policy would be
to apply algorithms from the literature that solve the closely-related
stochastic multi-armed bandit problem. These policies would focus on maximizing
the number of successful frame transmissions over time. However, we show that
these methods have queue length regret. On the other hand, we
show that there exists a set of queue-length based policies that can obtain
order optimal queue length regret. We use our theoretical analysis to
devise heuristic methods that are shown to perform well in simulation.Comment: 28 Pages, 11 figure
Concave Switching in Single and Multihop Networks
Switched queueing networks model wireless networks, input queued switches and
numerous other networked communications systems. For single-hop networks, we
consider a {()-switch policy} which combines the MaxWeight policies
with bandwidth sharing networks -- a further well studied model of Internet
congestion. We prove the maximum stability property for this class of
randomized policies. Thus these policies have the same first order behavior as
the MaxWeight policies. However, for multihop networks some of these
generalized polices address a number of critical weakness of the
MaxWeight/BackPressure policies.
For multihop networks with fixed routing, we consider the Proportional
Scheduler (or (1,log)-policy). In this setting, the BackPressure policy is
maximum stable, but must maintain a queue for every route-destination, which
typically grows rapidly with a network's size. However, this proportionally
fair policy only needs to maintain a queue for each outgoing link, which is
typically bounded in number. As is common with Internet routing, by maintaining
per-link queueing each node only needs to know the next hop for each packet and
not its entire route. Further, in contrast to BackPressure, the Proportional
Scheduler does not compare downstream queue lengths to determine weights, only
local link information is required. This leads to greater potential for
decomposed implementations of the policy. Through a reduction argument and an
entropy argument, we demonstrate that, whilst maintaining substantially less
queueing overhead, the Proportional Scheduler achieves maximum throughput
stability.Comment: 28 page
Optimized Surface Code Communication in Superconducting Quantum Computers
Quantum computing (QC) is at the cusp of a revolution. Machines with 100
quantum bits (qubits) are anticipated to be operational by 2020
[googlemachine,gambetta2015building], and several-hundred-qubit machines are
around the corner. Machines of this scale have the capacity to demonstrate
quantum supremacy, the tipping point where QC is faster than the fastest
classical alternative for a particular problem. Because error correction
techniques will be central to QC and will be the most expensive component of
quantum computation, choosing the lowest-overhead error correction scheme is
critical to overall QC success. This paper evaluates two established quantum
error correction codes---planar and double-defect surface codes---using a set
of compilation, scheduling and network simulation tools. In considering
scalable methods for optimizing both codes, we do so in the context of a full
microarchitectural and compiler analysis. Contrary to previous predictions, we
find that the simpler planar codes are sometimes more favorable for
implementation on superconducting quantum computers, especially under
conditions of high communication congestion.Comment: 14 pages, 9 figures, The 50th Annual IEEE/ACM International Symposium
on Microarchitectur
An Energy-Efficient Near-Data Processing Accelerator for DNNs that Optimizes Data Accesses
The constant growth of DNNs makes them challenging to implement and run
efficiently on traditional compute-centric architectures. Some accelerators
have attempted to add more compute units and on-chip buffers to solve the
memory wall problem without much success, and sometimes even worsening the
issue since more compute units also require higher memory bandwidth. Prior
works have proposed the design of memory-centric architectures based on the
Near-Data Processing (NDP) paradigm. NDP seeks to break the memory wall by
moving the computations closer to the memory hierarchy, reducing the data
movements and their cost as much as possible. The 3D-stacked memory is
especially appealing for DNN accelerators due to its high-density/low-energy
storage and near-memory computation capabilities to perform the DNN operations
massively in parallel. However, memory accesses remain as the main bottleneck
for running modern DNNs efficiently.
To improve the efficiency of DNN inference we present QeiHaN, a hardware
accelerator that implements a 3D-stacked memory-centric weight storage scheme
to take advantage of a logarithmic quantization of activations. In particular,
since activations of FC and CONV layers of modern DNNs are commonly represented
as powers of two with negative exponents, QeiHaN performs an implicit in-memory
bit-shifting of the DNN weights to reduce memory activity. Only the meaningful
bits of the weights required for the bit-shift operation are accessed. Overall,
QeiHaN reduces memory accesses by 25\% compared to a standard memory
organization. We evaluate QeiHaN on a popular set of DNNs. On average, QeiHaN
provides speedup and energy savings over a Neurocube-like
accelerator
Energy regeneration from suspension dynamic modes and self-powered actuation
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper concerns energy harvesting from vehicle suspension systems. The generated power associated with bounce, pitch and roll modes of vehicle dynamics is determined through analysis. The potential values of power generation from these three modes are calculated. Next, experiments are carried out using a vehicle with a four jack shaker rig to validate the analytical values of potential power harvest. For the considered vehicle, maximum theoretical power values of 1.1kW, 0.88kW and 0.97kW are associated with the bounce, pitch and roll modes, respectively, at 20 Hz excitation frequency and peak to peak displacement amplitude of 5 mm at each wheel, as applied by the shaker. The corresponding experimentally power values are 0.98kW, 0.74kW and 0.78kW. An experimental rig is also developed to study the behavior of regenerative actuators in generating electrical power from kinetic energy. This rig represents a quarter-vehicle suspension model where the viscous damper in the shock absorber system is replaced by a regenerative system. The rig is able to demonstrate the actual electrical power that can be harvested using a regenerative system. The concept of self-powered actuation using the harvested energy from suspension is discussed with regard to applications of self-powered vibration control. The effect of suspension energy regeneration on ride comfort and road handling is presented in conjunction with energy harvesting associated with random road excitations.Peer reviewedFinal Accepted Versio
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-HĂŒbner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro PezzĂ©, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
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