34887568
Memory access driven storage assignment for variables in embedded system design
42109913
An efficient low-power binding algorithm in high-level synthesis
We propose an efficient binding algorithm for power optimization in high-level synthesis. In prior work, it has been shown that several binding problems for low-power can be formulated as multi-commodity flow problems (due to an iterative execution of data flow graph) and be solved optimally. However, since the multi-commodity flow problem is NP-hard, the application is limited to a class of small sized problems. To overcome the limitation, we address the problem of how we can effectively make use of the property of efficient flow computations in a network so that it is extensively applicable to practical designs while producing close-to-optimal results. To this end, we propose an efficient two-step algorithm, which (1) determines a feasible binding solution by partially utilizing the computation steps for finding a maximum flow of minimum cost in a network and then (2) refines it iteratively. Experiments with a set of benchmark examples show that the proposed algorithm saves the run time significantly while maintaining close-to-optimal bindings in most practical designs.
195882206
Memory layout techniques for variables utilizing efficient DRAM access modes in embedded system design
The delay of memory access is one of the major bottlenecks in embedded systems' performance. In software compilation, it is known that there are high variations in memory access delay depending on the ways of storing/retrieving the variables in code to/from the memories. In this paper, we propose an effective storage assignment technique for variables to maximize the use of memory bandwidth. Specifically, we study the problem of DRAM memory layout for storing the nonarray variables in code to achieve a maximum utilization of page and/or burst modes for the memory accesses. The contributions of our work are, for each of page and burst modes: (1) We prove that the problem is NP-hard; (2) We propose an exact formulation of the problem and efficient memory layout algorithms, called Solve-MLP for the page mode and Solve-MLB for the burst mode; From experiments with a set of benchmark programs, we confirm that our proposed techniques use on average 20.0% and 9.9% more page accesses and 54.0% and 86.6% more burst accesses than those by OFU (the order of first use) and the technique in P.R. Panda et al. (1997), respectively.
15526593
Low-energy off-chip SDRAM memory systems for embedded applications
Memory systems are dominant energy consumers, and thus many energy reduction techniques for memory buses and devices have been proposed. For practical energy reduction practices, we have to take into account the interaction between a processor and cache memories together with application programs. Furthermore, energy characterization of memory systems must be accurate enough to justify various techniques. In this article, we build an in-house energy simulator for memory systems that is accelerated by special hardware support while maintaining accuracy. We explore energy behavior of memory systems for various values of the processor and memory clock frequencies and cache configuration. Each experiment is performed with 24M instruction steps of real application programs to guarantee accuracy.The simulator is based on precise energy characterization of memory systems including buses, bus drivers, and memory devices by a cycle-accurate energy measurement technique. We characterize energy consumption of each component by an energy state machine whose states and transitions are associated with the dynamic and static energy costs, respectively. Our approach easily characterizes the energy consumption of complex SDRAMs. We divide and quantify energy components of main memory systems for high-level reduction. The energy simulator enables us to devise practical energy reduction schemes by providing the actual amount of reduction out of the total energy consumption in main memory systems. We introduce several practical energy reduction techniques for SDRAM memory systems and demonstrate energy reduction ratio over the SDRAM memory systems with commercial SDRAM controller chipsets. We classify the SDRAM memory systems into high-performance and mid-performance classes and achieve suitable system configurations for each class. For instance, a typical high-performance 32-bit, 64 MB SDRAM memory system consumes 19.6 mJ, 33.8 mJ, 35.4 mJ, and 37.0 mJ for 24M instructions of an MP3 decoder, a JPEG compressor, a JPEG decompressor, and an MPEG4 decoder, respectively. Our reduction scheme saves 12.7 mJ, 15.1 mJ, 15.5 mJ, and 14.8 mJ, and the reduction ratios are 64.8%, 44.6%, 43.8%, and 40.1%, respectively, without compromising execution speed.
154999
Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns
Recent systems have been developed for sentiment classification, opinion recognition, and opinion analysis (e.g., detecting polarity and strength). We pursue another aspect of opinion analysis: identifying the sources of opinions, emotions, and sentiments. We view this problem as an information extraction task and adopt a hybrid approach that combines Conditional Random Fields (Lafferty et al., 2001) and a variation of AutoSlog (Riloff, 1996a). While CRFs model source identification as a sequence tagging task, AutoSlog learns extraction patterns. Our results show that the combination of these two methods performs better than either one alone. The resulting system identifies opinion sources with 79.3% precision and 59.5% recall using a head noun matching measure, and 81.2% precision and 60.6% recall using an overlap measure.
12470418
Genetic Fuzzy Discretization for Classification Problems
Many real-world classification algorithms can not be applied unless the continuous attributes are discretized and the interval discretization methods are used in many machine learning techniques. It is hard to determine the intervals for the discretization of numerical attributes that has an infinite number of candidates. And interval discretization methods are based on a crisp set, a value in a continuous attribute must belong to only one interval. They are often not proper for describing a value located around the boundaries of intervals. Fuzzy partioning is an attractive method for those cases in classification problems. An important decision in fuzzy partitioning is about the positions of interval boundaries and the degrees of overlapping in the fuzzy sets. We optimize the parameters that specify fuzzy partitioning by genetic algorithms.
6312845
Energy exploration and reduction of SDRAM memory systems
In this paper, we introduce a precise energy characterization of SDRAM main memory systems and explore the amount of energy associated with design parameters, leading to energy reduction techniques that we are able to recommend for practical use.We build an in-house energy simulator for SDRAM main memory systems based on cycle-accurate energy measurement and state-machine-based characterizations which independently characterize dynamic and static energy. We explore energy behavior of the memory systems by changing design parameters such as processor clock, memory clock and cache configuration. Finally we propose new energy reduction techniques for the address bus and practical mode control schemes for the SDRAM devices. We save 10.8mJ and 12mJ, 40.2% and 14.5% of the total energy, for 24M instructions of an MP3 decoder and a JPEG compressor, using a typical 32-bit, 64MB SDRAM memory system.
1609683
Web-Based Energy Exploration Tool for Embedded Systems
We describe a Web-based energy estimation tool for embedded systems. An interesting feature of this tool is that it performs real-time cycle-accurate energy measurements on a hardware prototype of the processor. The authors describe the various steps involved in using the tool and present case studies to illustrate its utility.