Frame-Based Dynamic Voltage and Frequency Scaling for an MPEG Player
This paper describes a dynamic voltage and frequency scaling (DVFS) technique for MPEG decoding to reduce the energy consumption while maintaining a quality of service (QoS) constraint. The computational workload for an incoming frame is predicted by using a frame-based history so that the processor voltage and frequency can be scaled to provide the exact amount of computing power needed to decode the frame. More precisely, the required decoding time for each frame is separated into two parts: a frame-dependent (FD) part and a frame-independent (FI) part. The FD part varies greatly according to the type of the incoming frame whereas the FI part remains constant regardless of the frame type. Separation of the FI part from the overall decoding sequence provides two key benefits depending on the hardware platform: better compensation of the error due to workload prediction and higher level of energy saving when given a QoS degradation level. The proposed DVFS scheme has been implemented on two platforms, a low performance StrongArm-1110-based evaluation board from Intel and a high performance XScale-based testbed designed at USC. In the StrongArm-1110-based system, the FI part is used to compensate for the prediction error that may occur during the FD part, whereas in the XScale-based system, the FI part is used to reduce energy consumption by employing the lowest CPU frequency during the corresponding time intervals. Detailed current measurements in these two platforms demonstrate larger than 87 % and 80 % CPU energy saving while maintaining a user-provided frame rate, respectively. Index Terms — Dynamic voltage and frequency scaling, MPEG decoding, workload decomposition.

title: A game theoretic approach to low energy wireless video streaming
abstract: This paper presents a dynamic energy management policy for a wireless video streaming system, consisting of battery-powered client and server. The paper starts from the observation that the video quality in wireless streaming is a function of three factors: encoding aptitude of the server, decoding aptitude of the client, and the wireless channel. Based on this observation, the energy consumption of a wireless video streaming system is modeled and analyzed. Using the proposed model, the optimal energy assignment to each video frame is done such that the maximum system lifetime is achieved while satisfying a given minimum video quality requirement. Experimental results show that the proposed policy increases the system lifetime by 20%.

title: Fine-Grained Dynamic Voltage and Frequency Scaling for Precise Energy and Performance Trade-Off Based on the Ratio of Off-Chip Access to On-Chip Computation Times
abstract: This paper presents an intra-process dynamic voltage and frequency scaling (DVFS) technique targeted toward non real-time applications running on an embedded system platform. The key idea is to make use of runtime information about the external memory access statistics in order to perform CPU voltage and frequency scaling with the goal of minimizing the energy consumption while translucently controlling the performance penalty. The proposed DVFS technique relies on dynamically-constructed regression models that allow the CPU to calculate the expected workload and slack time for the next time slot, and thus, adjust its voltage and frequency in order to save energy while meeting soft timing constraints. This is in turn achieved by estimating and exploiting the ratio of the total off-chip access time to the total on-chip computation time. The proposed technique has been implemented on an XScale-based embedded system platform and actual energy savings have been calculated by current measurements in hardware. For memory-bound programs, a CPU energy saving of more than 70% with a performance degradation of 12% was achieved. For CPU-bound programs, 15/spl sim/60% CPU energy saving was achieved at the cost of 5-20% performance penalty.

title: Energy-Aware Wireless Video Streaming
abstract: This paper presents a dynamic energy management policy for a wireless video streaming system, consisting of battery-powered client and server. The paper starts from the observation that the video quality in wireless streaming is a function of three factors: encoding aptitude of the server, decoding aptitude of the client, and the wireless channel. Based on this observation, the energy consumption of a wireless video streaming system is modeled and analyzed. Using the proposed model, the optimal energy assignment to each video frame is done such that the maximum system lifetime is achieved while satisfying a given minimum video quality requirement. Experimental results show that the proposed policy increases the system lifetime by 20%.

title: Off-chip latency-driven dynamic voltage and frequency scaling for an MPEG decoding
abstract: This paper describes a dynamic voltage and frequency scaling (DVFS) technique for MPEG decoding to reduce the energy consumption using the computational workload decomposition. This technique decomposes the workload for decoding a frame into on-chip and off-chip workloads. The execution time required for the on-chip workload is CPU frequency-dependent, whereas the off-chip workload execution time does not change, regardless of the CPU frequency, resulting in the maximum energy savings by setting the minimum frequency during off-chip workload execution time, without causing any delay penalty. This workload decomposition is performed using a performance-monitoring unit (PMU) in the XScale-processor, which provides various statistics such as cache hit/miss and CPU stall, due to data dependency at run time. The on-chip workload for an incoming frame is predicted using a frame-based history so that the processor voltage and frequency can be scaled to provide the exact amount of computing power needed to decode the frame. To guarantee a quality of service (QoS) constraint, a prediction error compensation method, called inter-frame compensation, is proposed in which the on-chip workload prediction error is diffused into subsequent frames such that run time frame rates change smoothly. The proposed DVFS algorithm has been implemented on an XScale-based Testbed. Detailed current measurements on this platform demonstrate significant CPU energy savings ranging from 50% to 80% depending on the video clip.

title: Dynamic voltage and frequency scaling based on workload decomposition
abstract: This paper presents a technique called "workload decomposition" in which the CPU workload is decomposed in two parts: on-chip and off-chip. The on-chip workload signifies the CPU clock cycles that are required to execute instructions in the CPU whereas the off-chip workload captures the number of external memory access clock cycles that are required to perform external memory transactions. When combined with a dynamic voltage and frequency scaling (DVFS) technique to minimize the energy consumption, this workload decomposition method results in higher energy savings. The workload decomposition itself is performed at run time based on statistics reported by a performance monitoring unit (PMU) without a need for application profiling or compiler support. We have implemented the proposed DVFS with workload decomposition technique on the BitsyX platform, an Intel PXA255-based platform manufactured by ADS Inc., and performed detailed energy measurements. These measurements show that, for a number of widely used software applications, a CPU energy saving of 80% can be achieved for memory-bound programs while satisfying the user-specified timing constraints.

title: PLATCOM: a Platform for Computational Comparative Genomics
abstract: As more whole genome sequences become available, comparing multiple genomes at the sequence level can provide insight into new biological discovery. However, there are significant challenges for genome comparison. The challenge includes requirement for computational resources owing to the large volume of genome data. More importantly, since the choice of genomes to be compared is entirely subjective, there are too many choices for genome comparison. For these reasons, there is pressing need for bioinformatics systems for comparing multiple genomes where users can choose genomes to be compared freely.



PLATCOM (Platform for Computational Comparative Genomics) is an integrated system for the comparative analysis of multiple genomes. The system is built on several public databases and a suite of genome analysis applications are provided as exemplary genome data mining tools over these internal databases. Researchers are able to visually investigate genomic sequence similarities, conserved gene neighborhoods, conserved metabolic pathways and putative gene fusion events among a set of selected multiple genomes.



http://platcom.informatics.indiana.edu/platcom

title: Efficient instruction-level optimization methodology for low-power embedded systems
abstract: For low-power embedded systems, we solve the instruction scheduling and reordering problem as a Precedence Constrained Hamiltonian Path Problem for DAGs and the traveling salesman problem (TSP), both of which are NP-hard (W.J. Cook et al., 1998; V. Jain et al., 1999). We propose an efficient instruction-level optimization algorithm for solving the NP-hard problem. Minimum spanning tree (MST) and simulated annealing (SA) mechanisms are used for the optimization. We describe the methods for generating the control flow and data dependence graph (CDG), power dissipation table (PDT), and weighted strongly connected graph (SCG) for the instruction-level low-power analysis. In addition, confidence limits with error tolerance are considered for the validation of the optimization. Finally, experimental results that demonstrate the effectiveness and efficiency of the proposed algorithms are shown.

title: Sensitivity analysis and efficient method for identifying optimal spaced seeds
abstract: The novel introduction of spaced seed idea in the filtration stage of sequence comparison by Ma et al. (Bioinformatics 18 (2002) 440) has greatly increased the sensitivity of homology search without compromising the speed of search. Finding the optimal spaced seeds is of great importance both theoretically and in designing better search tool for sequence comparison. In this paper, we study the computational aspects of calculating the hitting probability of spaced seeds; and based on these results, we propose an efficient algorithm for identifying optimal spaced seeds.

title: An ensemble of transliteration models for information retrieval
abstract: Transliteration is used to phonetically translate proper names and technical terms especially from languages in Roman alphabets to languages in non-Roman alphabets such as from English to Korean, Japanese, and Chinese. Because transliterations are usually representative index terms for documents, proper handling of the transliterations is important for an effective information retrieval system. However, there are limitations on handling transliterations depending on dictionary lookup, because transliterations are usually not registered in the dictionary. For this reason, many researchers have been trying to overcome the problem using machine transliteration. In this paper, we propose a method for improving machine transliteration using an ensemble of three different transliteration models. Because one transliteration model alone has limitation on reflecting all possible transliteration behaviors, several transliteration models should be complementary used in order to achieve a high-performance machine transliteration system. This paper describes a method about transliteration production using the several machine transliteration models and transliteration ranking with web data and relevance scores given by each transliteration model. We report evaluation results for our ensemble transliteration model and experimental results for its impact on IR effectiveness. Machine transliteration tests on English-to-Korean transliteration and English-to-Japanese transliteration show that our proposed method achieves 78-80% word accuracy. Information retrieval tests on KTSET and NTCIR-1 test collection show that our transliteration model can improve the performance of an information retrieval system about 10-34%.

title: System level power-performance trade-offs in embedded systems using voltage and frequency scaling of off-chip buses and memory
abstract: In embedded systems, off-chip buses and memory (i.e., L2 memory as opposed to the L1 memory which is usually on-chip cache) consume significant power often more than the processor itself. In this paper for the case of an embedded system with one processor chip and one memory chip, we propose frequency and voltage scaling of the off-chip buses and the memory chip and use a known micro-architectural enhancement called a store buffer to reduce the resulting impact on execution time. Our benchmarks show a system (processor + off-chip bus + off-chip memory) power savings of 28% to 36%, an energy savings of 13% to 35%, all while increasing the execution time in the range of 1% to 29%. Previous work in power-aware computing has focused on frequency and voltage scaling of the processors or selective power-down of sub-sets of off-chip memory chips. This paper quantitatively explores voltage/frequency scaling of off-chip buses and memory as a means of trading off performance for power/energy at the system level in embedded systems.