5,737 research outputs found
Privacy Leakages in Approximate Adders
Approximate computing has recently emerged as a promising method to meet the
low power requirements of digital designs. The erroneous outputs produced in
approximate computing can be partially a function of each chip's process
variation. We show that, in such schemes, the erroneous outputs produced on
each chip instance can reveal the identity of the chip that performed the
computation, possibly jeopardizing user privacy. In this work, we perform
simulation experiments on 32-bit Ripple Carry Adders, Carry Lookahead Adders,
and Han-Carlson Adders running at over-scaled operating points. Our results
show that identification is possible, we contrast the identifiability of each
type of adder, and we quantify how success of identification varies with the
extent of over-scaling and noise. Our results are the first to show that
approximate digital computations may compromise privacy. Designers of future
approximate computing systems should be aware of the possible privacy leakages
and decide whether mitigation is warranted in their application.Comment: 2017 IEEE International Symposium on Circuits and Systems (ISCAS
Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters
Demands on the disaster response capacity of the European Union are likely to
increase, as the impacts of disasters continue to grow both in size and
frequency. This has resulted in intensive research on issues concerning
spatially-explicit information and modelling and their multiple sources of
uncertainty. Geospatial support is one of the forms of assistance frequently
required by emergency response centres along with hazard forecast and event
management assessment. Robust modelling of natural hazards requires dynamic
simulations under an array of multiple inputs from different sources.
Uncertainty is associated with meteorological forecast and calibration of the
model parameters. Software uncertainty also derives from the data
transformation models (D-TM) needed for predicting hazard behaviour and its
consequences. On the other hand, social contributions have recently been
recognized as valuable in raw-data collection and mapping efforts traditionally
dominated by professional organizations. Here an architecture overview is
proposed for adaptive and robust modelling of natural hazards, following the
Semantic Array Programming paradigm to also include the distributed array of
social contributors called Citizen Sensor in a semantically-enhanced strategy
for D-TM modelling. The modelling architecture proposes a multicriteria
approach for assessing the array of potential impacts with qualitative rapid
assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema
and complementing more traditional and accurate a-posteriori assessment. We
discuss the computational aspect of environmental risk modelling using
array-based parallel paradigms on High Performance Computing (HPC) platforms,
in order for the implications of urgency to be introduced into the systems
(Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of
Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua
Evaluating critical bits in arithmetic operations due to timing violations
Various error models are being used in simulation of voltage-scaled arithmetic units to examine application-level tolerance of timing violations. The selection of an error model needs further consideration, as differences in error models drastically affect the performance of the application. Specifically, floating point arithmetic units (FPUs) have architectural characteristics that characterize its behavior. We examine the architecture of FPUs and design a new error model, which we call Critical Bit. We run selected benchmark applications with Critical Bit and other widely used error injection models to demonstrate the differences
Optimizing for confidence - Costs and opportunities at the frontier between abstraction and reality
Is there a relationship between computing costs and the confidence people
place in the behavior of computing systems? What are the tuning knobs one can
use to optimize systems for human confidence instead of correctness in purely
abstract models? This report explores these questions by reviewing the
mechanisms by which people build confidence in the match between the physical
world behavior of machines and their abstract intuition of this behavior
according to models or programming language semantics. We highlight in
particular that a bottom-up approach relies on arbitrary trust in the accuracy
of I/O devices, and that there exists clear cost trade-offs in the use of I/O
devices in computing systems. We also show various methods which alleviate the
need to trust I/O devices arbitrarily and instead build confidence
incrementally "from the outside" by considering systems as black box entities.
We highlight cases where these approaches can reach a given confidence level at
a lower cost than bottom-up approaches.Comment: 11 pages, 1 figur
XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge
Bio-signals exhibit high redundancy, and the algorithms for their processing
are inherently error resilient. This property can be leveraged to improve the
energy-efficiency of IoT-Edge (wearables) through the emerging trend of
approximate computing. This paper presents XBioSiP, a novel methodology for
approximate bio-signal processing that employs two quality evaluation stages,
during the pre-processing and bio-signal processing stages, to determine the
approximation parameters. It thereby achieves high energy savings while
satisfying the user-determined quality constraint. Our methodology achieves, up
to 19x and 22x reduction in the energy consumption of a QRS peak detection
algorithm for 0% and <1% loss in peak detection accuracy, respectively.Comment: Accepted for publication at the Design Automation Conference 2019
(DAC'19), Las Vegas, Nevada, US
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