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Energy Aware Lossless Data Compression
 MOBISYS 2003
, 2003
"... Wireless transmission of a bit can require over 1000 times more energy than a single 32bit computation. It would therefore seem desirable to perform significant computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send it, ..."
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Cited by 74 (0 self)
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Wireless transmission of a bit can require over 1000 times more energy than a single 32bit computation. It would therefore seem desirable to perform significant computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send it, there is a net energy savings and consequently, a longer battery life for portable computers. This paper reports on the energy of lossless data compressors as measured on a StrongARM SA110 system. We show that with several typical compression tools, there is a net energy increase when compression is applied before transmission. Reasons for this increase are explained, and hardwareaware programming optimizations are demonstrated. When applied to Unix compress, these optimizations improve energy efficiency by 51%. We also explore the fact that, for many usage models, compression and decompression need not be performed by the same algorithm. By choosing the lowestenergy compressor and decompressor on the test platform, rather than using default levels of compression, overall energy to send compressible web data can be reduced 31%. Energy to send hardertocompress English text can be reduced 57%. Compared with a system using a single optimized application for both compression and decompression, the asymmetric scheme saves 11% or 12% of the total energy depending on the dataset.
Energyaware lossless data compression
 ACM Trans. Computer Systems
, 2006
"... Wireless transmission of a single bit can require over 1000 times more energy than a single 32bit computation. It can therefore be beneficial to perform additional computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send ..."
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Cited by 23 (0 self)
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Wireless transmission of a single bit can require over 1000 times more energy than a single 32bit computation. It can therefore be beneficial to perform additional computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send it, there is a net energy savings and an increase in battery life for portable computers. This article presents a study of the energy savings possible by losslessly compressing data prior to transmission. A variety of algorithms were measured on a StrongARM SA110 processor. This work demonstrates that, with several typical compression algorithms, there is a actually a net energy increase when compression is applied before transmission. Reasons for this increase are explained and suggestions are made to avoid it. One such energyaware suggestion is asymmetric compression, the use of one compression algorithm on the transmit side and a different algorithm for the receive path. By choosing the lowestenergy compressor and decompressor on the test platform, overall energy to send and receive data can be reduced by 11 % compared with a wellchosen symmetric pair, or up to 57 % over the default symmetric zlib scheme.
An energyaware framework for coordinated dynamic software management in mobile computers
 In Proceedings of Intl. Symp. on Modeling, Analysis and Simulation of Computer and Telecommunications Systems
, 2004
"... Energy efficiency is a very important and challenging issue for resourceconstrained mobile computers. In this paper, we propose a dynamic software management (DSM) framework to improve battery utilization, and avoid competition for limited energy resources from multiple applications. We have design ..."
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Cited by 12 (1 self)
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Energy efficiency is a very important and challenging issue for resourceconstrained mobile computers. In this paper, we propose a dynamic software management (DSM) framework to improve battery utilization, and avoid competition for limited energy resources from multiple applications. We have designed and implemented a DSM module in user space, independent of the operating system (OS), which explores qualityofservice (QoS) adaptation to reduce system energy and employs a prioritybased preemption policy for multiple applications. It also employs energy macromodels for mobile applications to aid in this endeavor. By monitoring the energy supply and predicting energy demand at each QoS level, the DSM module is able to select the best possible tradeoff between energy conservation and application QoS. To the best of our knowledge, this is the first energyaware coordinated framework utilizing adaptation of mobile applications. It honors the priority desired by the user and is portable to POSIXcompliant OSs. Our experimental results for some mobile applications (video player, speech recognizer, voiceoverIP) show that this approach can meet userspecified taskoriented goals and improve battery utilization significantly. They also show that prediction of application energy demand based on energy macromodels is a key component of this framework. 1
Optimizing polynomial expressions by algebraic factorization and common subexpression elimination
 IEEE Trans. on CAD of Integrated Circuits and Systems
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Factoring and eliminating common subexpressions in polynomial expressions
 in ICCAD
, 2004
"... Polynomial expressions are used to compute a wide variety of mathematical functions commonly found in signal processing and graphics applications, which provide good opportunities for optimization. However existing compiler techniques for reducing code complexity such as common subexpression elimina ..."
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Cited by 9 (0 self)
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Polynomial expressions are used to compute a wide variety of mathematical functions commonly found in signal processing and graphics applications, which provide good opportunities for optimization. However existing compiler techniques for reducing code complexity such as common subexpression elimination and value numbering are targeted towards general purpose applications and are unable to fully optimize these expressions. This paper presents algorithms to reduce the number of operations to compute a set of polynomial expression by factoring and eliminating common subexpressions. These algorithms are based on the algebraic techniques for multilevel logic synthesis. Experimental results on a set of benchmark applications with polynomial expressions showed an average of 42.5 % reduction in the number of multiplications and 39.6% reduction in the number of clock cycles for computation of these expressions on the ARM processor core, compared to common subexpression elimination. 1.
BEnergy Efficient Hardware Synthesis of Polynomial Expressions,^ presented at
 the International Conference on VLSI Design
"... Polynomial expressions are used to approximate a wide variety of functions commonly found in signal processing and computer graphics applications. Computing these polynomial expressions in hardware consumes a lot of energy and therefore careful optimization of these expressions is important in order ..."
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Cited by 5 (1 self)
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Polynomial expressions are used to approximate a wide variety of functions commonly found in signal processing and computer graphics applications. Computing these polynomial expressions in hardware consumes a lot of energy and therefore careful optimization of these expressions is important in order to achieve low energy consumption. Unfortunately, current optimization techniques for reducing complexity of expressions such as Common Subexpression Elimination (CSE) cannot do a good optimization. In this paper, we present an algebraic technique to reduce the energy consumption of custom datapath implementation of polynomials by reducing the number of energy intensive operations. Our techniques can handle polynomial expressions of any order and containing any number of variables. Synthesis of a set of benchmark polynomials verified the advantages of our technique in reducing energy consumption, where we observed up to 58% improvement over CSE.
Complex Library Mapping for Embedded Software Using Symbolic Algebra
, 2002
"... Embedded software designers often use libraries that have been preoptimized for a given processor to achieve higher code quality. However, using such libraries in legacy code optimization is nontrivial and typically requires manual intervention. This paper presents a methodology that maps algorithm ..."
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Cited by 3 (1 self)
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Embedded software designers often use libraries that have been preoptimized for a given processor to achieve higher code quality. However, using such libraries in legacy code optimization is nontrivial and typically requires manual intervention. This paper presents a methodology that maps algorithmic constructs of the software specification to a library of complex software elements. This librarymapping step is automated by using symbolic algebra techniques. We illustrate the advantages of our methodology by optimizing an algorithmic level description of MPEG Layer III (MP3) audio decoder for the Badge4 [2] portable embedded system. During the optimization process we use commercially available libraries with complex elements ranging from simple mathematical functions such as exp to the IDCT routine. We implemented and measured the performance and energy consumption of the MP3 decoder software on Badge4 running embedded Linux operating system. The optimized MP3 audio decoder runs 300 times faster than the original code obtained from the standards body while consuming 400 times less energy. Since our optimized MP3 decoder runs 3.5 times faster than realtime, additional energy can be saved by using processor frequency and voltage scaling.
Embedded Program Specialization for Multiple Criteria Tradeoffs
, 2008
"... As a result of evergrowing processing energy, larger storage space and faster communication channels, modern embedded systems (ES) are predominantly pervasive and mobile. However, there are heavy constraints on software ..."
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Cited by 2 (2 self)
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As a result of evergrowing processing energy, larger storage space and faster communication channels, modern embedded systems (ES) are predominantly pervasive and mobile. However, there are heavy constraints on software
DESIGN OF ONTOLOGYBASED GENERATIVE COMPONENTS USING ENRICHED FEATURE DIAGRAMS AND META PROGRAMMING
"... Abstract. A product line (PL) approach is emerging as the most promising design paradigm for embedded software design domain, where a great variability of requirements and products exists. The implementation of the PL approach requires thorough domain analysis and domain modelling. We propose to rep ..."
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Abstract. A product line (PL) approach is emerging as the most promising design paradigm for embedded software design domain, where a great variability of requirements and products exists. The implementation of the PL approach requires thorough domain analysis and domain modelling. We propose to represent embedded software components using Enriched Feature Diagrams (EFDs). EFDs are an extension of traditional Feature Diagrams (FDs) for explicit representation of domain variability enriched with contextualization and domain ontology. We suggest to transform feature models described using EFDs into generative component specifications encoded using the metaprogramming techniques. A case study from the embedded software specialization domain is presented.
International Journal of Electrical and Computer Engineering 1:1 2006 ObjectOriented Programming Strategies in C# for Power Conscious System
"... Abstract—Low power consumption is a major constraint for batterypowered system like computer notebook or PDA. In the past, specialists usually designed both specific optimized equipments and codes to relief this concern. Doing like this could work for quite a long time, however, in this era, there ..."
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Abstract—Low power consumption is a major constraint for batterypowered system like computer notebook or PDA. In the past, specialists usually designed both specific optimized equipments and codes to relief this concern. Doing like this could work for quite a long time, however, in this era, there is another significant restraint, the time to market. To be able to serve along the power constraint while can launch products in shorter production period, objectoriented programming (OOP) has stepped in to this field. Though everyone knows that OOP has quite much more overhead than assembly and procedural languages, development trend still heads to this new world, which contradicts with the target of low power consumption. Most of the prior power related software researches reported that OOP consumed much resource, however, as industry had to accept it due to business reasons, up to now, no papers yet had mentioned about how to choose the best OOP practice in this power limited boundary. This article is the pioneer that tries to specify and propose the optimized strategy in writing OOP software under energy concerned environment, based on quantitative real results. The language chosen for studying is C # based on.NET Framework 2.0 which is one of the trendy OOP development environments. The recommendation gotten from this research would be a good roadmap that can help developers in coding that well balances between time to market and time of battery.