Wireless transmission of a bit can require over 1000 times more energy than a single 32-bit 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 SA-110 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 lowest-energy 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 harder-to-compress 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.

Energy efficiency is a very important and challenging issue for resource-constrained 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 quality-of-service (QoS) adaptation to reduce system energy and employs a priority-based 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 trade-off between energy conservation and application QoS. To the best of our knowledge, this is the first energy-aware coordinated framework utilizing adaptation of mobile applications. It honors the priority desired by the user and is portable to POSIX-compliant OSs. Our experimental results for some mobile applications (video player, speech recognizer, voice-over-IP) show that this approach can meet user-specified task-oriented goals and improve battery utilization significantly. They also show that prediction of application energy demand based on energy macro-models is a key component of this framework. 1

Wireless transmission of a single bit can require over 1000 times more energy than a single 32-bit 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 SA-110 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 energy-aware 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 lowest-energy compressor and decompressor on the test platform, overall energy to send and receive data can be reduced by 11 % compared with a well-chosen symmetric pair, or up to 57 % over the default symmetric zlib scheme.

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 multi-level 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.

Abstract—Polynomial expressions are frequently encountered in many application domains, particularly in signal processing and computer graphics. Conventional compiler techniques for redundancy elimination such as common subexpression elimination (CSE) are not suited for manipulating polynomial expressions, and designers often resort to hand optimizing these expressions. This paper leverages the algebraic techniques originally developed for multilevel logic synthesis to optimize polynomial expressions by factoring and eliminating common subexpressions. The proposed algorithm was tested on a set of benchmark polynomial expressions where savings of 26.7 % in latency and 26.4 % in energy consumption were observed for computing these expressions on the StrongARM SA1100 processor core. When these expressions were synthesized in custom hardware, average energy savings of 63.4 % for minimum hardware constraints and 24.6 % for medium hardware constraints over CSE were observed. Index Terms—Circuit complexity, common subexpression elimination (CSE), high-level synthesis, polynomials. I.

Embedded software designers often use libraries that have been pre-optimized 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 library-mapping 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 real-time, additional energy can be saved by using processor frequency and voltage scaling.

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.

Wireless transmission of a bit can require over 1000 times more energy than a single 32-bit 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 SA-110 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 lowest-energy 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 harder-to-compress 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.

Low power consumption is a major constraint for battery-powered 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.

Abstract—Design Patterns have gained more and more acceptances since their emerging in software development world last decade and become another de facto standard of essential knowledge for Object-Oriented Programming developers nowadays. Their target usage, from the beginning, was for regular computers, so, minimizing power consumption had never been a concern. However, in this decade, demands of more complicated software for running on mobile devices has grown rapidly as the much higher performance portable gadgets have been supplied to the market continuously. To get along with time to market that is business reason, the section of software development for power conscious, battery, devices has shifted itself from using specific low-level languages to higher level ones. Currently, complicated software running on mobile devices are often developed by high level languages those support OOP concepts. These cause the trend of embracing Design Patterns to mobile world. However, using Design Patterns directly in software development for power conscious systems is not recommended because they were not originally designed for such environment. This paper demonstrates the adapted Design Pattern for power limitation system. Because there are numerous original design patterns, it is not possible to mention the whole at once. So, this paper focuses only in creating