Global interconnect trade-off for technology over memory modules to application level

Papanikolaou, Apostolos; Miranda, M.; Catthoor, Francky; Corporaal, Henk; Man, H. De; Roest, David De; Stucchi, M.; Maex, Karen

doi:10.1145/639929.639954

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Separate, complementary methodologies can be used, in addition to our work, to reduce the energy of the instruction memory [4]. In the context of this paper, the energy estimations are calculated using the model in [25] and concern the energy dissipated by the memory. This model depends on memory footprint factors (i.e., memory size, internal structure of banks and sub-banks, memory leaks, working time of the memory and technology) and energy consumption factors created by memory accesses (i.e., number of memory accesses, energy consumption in active mode, size of the memory and technology).…”

Section: Dynamic Data Type Search Spacementioning

confidence: 99%

Systematic methodology for exploration of performance – Energy trade-offs in network applications using Dynamic Data Type refinement

Mamagkakis¹,

Bartzas

Pouiklis

et al. 2007

Journal of Systems Architecture

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Modern network applications require high performance and consume a lot of energy. Their inherent dynamic nature makes the dynamic memory subsystem a critical contributing factor to the overall energy consumption and to the execution time performance. This paper presents a novel, systematic methodology for generating performance-energy trade-offs by implementing optimal Dynamic Data Types, finely tuned and refined for network applications. Our systematic methodology is supported by a new, fully automated tool. We assess the effectiveness of the proposed approach in four representative, reallife case studies and provide significant energy savings and performance improvements compared to the original implementations.

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Section: Dynamic Data Type Search Spacementioning

confidence: 99%

Systematic methodology for exploration of performance – Energy trade-offs in network applications using Dynamic Data Type refinement

Mamagkakis¹,

Bartzas

Pouiklis

et al. 2007

Journal of Systems Architecture

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“…No accesses to instruction memories are measured in this paper since we do not focus on any concrete architecture and moreover the data accesses are the most important factor to optimize for new dynamic applications [7]. Finally, the energy estimations are made with the use of an updated version of the CACTI model [17]. This is a complete energy/delay/area model for embedded SRAMs that depends on memory usage factors (e.g., size, internal structure or leaks) and factors originated by memory accesses (e.g., number of accesses or technology node used).…”

Section: Automated Pareto-optimal Configurations Selectionmentioning

confidence: 99%

Energy-efficient dynamic memory allocators at the middleware level of embedded systems

Mamagkakis¹,

Atienza²,

Poucet

et al. 2006

Proceedings of the 6th ACM &Amp; IEEE International Conference on Embedded Software - EMSOFT '06

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The next generation of embedded systems will be dominated by mobile devices, which are able to deliver communications and rich multimedia content anytime, anywhere. The major themes in these ubiquitous computing systems are applications with increased user control and interactivity with the environment. Therefore, the storage of dynamic data increases, thus making the dynamic memory allocation of heap data at run time a very important component with heavy energy consumption. In this paper, we propose a novel script, which heavily customizes the dynamic memory allocator according to the target application domain and the underlying memory hierarchy of the embedded system. The dynamic memory allocator resides in the middleware level or in the Operating System level (whenever it is available). The result of our script and automated tools is the reduction of energy consumption by 72% on average and the reduction of the execution time by 40% on average, which is demonstrated with the use of 1 real life wireless network application and 1 multimedia application.

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“…Then, we automatically keep the combinations, which have the lowest energy consumption, shortest execution time, lowest memory footprint and lower memory accesses. The energy estimations are calculated using an updated version of the CACTI model [12]. According to our experimental results (Section 4) approximately 80% of the DDT combinations produce not optimal results for all the aforementioned metrics.…”

Section: Application-level Ddt Explorationmentioning

confidence: 99%

Dynamic data type refinement methodology for systematic performance-energy design exploration of network applications

Bartzas

Mamagkakis

Pouiklis

et al. 2006

Proceedings of the Design Automation &Amp; Test in Europe Conference

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Network applications are becoming increasingly popular in the embedded systems domain requiring high performance, which leads to high energy consumption. In networks is observed that due to their inherent dynamic nature the dynamic memory subsystem is a main contributor to the overall energy consumption and performance. This paper presents a new systematic methodology, generating performance-energy trade-offs by implementing Dynamic Data Types (DDTs), targeting network applications. The proposed methodology consists of: (i) the application-level DDT exploration, (ii) the network-level DDT exploration and (iii) the Pareto-level DDT exploration. The methodology, supported by an automated tool, offers the designer a set of optimal dynamic data type design solutions. The effectiveness of the proposed methodology is tested on four representative real-life case studies. By applying the second step, it is proved that energy savings up to 80% and performance improvement up to 22% (compared to the original implementations of the benchmarks) can be achieved. Additional energy and performance gains can be achieved and a wide range of possible trade-offs among our Pareto-optimal design choices are obtained, by applying the third step. We achieved up to 93% reduction in energy consumption and up to 48% increase in performance. . IntroductionIn the last years, there is a trend toward networks and network applications implemented with the use of embedded consumer devices. The complexity of modern wired and wireless networks combined with the increased interaction with the environment (e.g. in wireless networks) has increased the dynamism of the data access pattern.The dynamism of network applications depends on various factors. One of them is the network traffic. Depending on that, the behavior of functions, as well as the number of times they are executed, differ. The amount of stored data, needed for these functions, varies as well. Thus, dynamically adjustable storage size is a necessity, allowing for freeing of memory when it is no longer needed. Moreover, a static memory allocation at compile time is not an option in the case of most modern network applications, because they do not have a static data access behavior, but rather a dynamic one. This fact means that a static memory allocation at compile time is not efficient at all, because the worst case situation has to be assumed in the beginning and implemented for the whole execution time. Therefore, dynamic memory allocation and management is required (especially in embedded systems). This leads to an increased reliance on Dynamic Data Types (DDTs from now on), the structures, which allow data to be dynamically allocated and deallocated at run-time and provide an easy way for the designer to connect, access and process data [17]. DDTs (with the most common examples being the single and doubly linked lists) can efficiently cope with the variations of runtime needs (e.g. network traffic, user interaction) and the massive amounts of transferred and stored data.Ine...

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Global interconnect trade-off for technology over memory modules to application level

Cited by 9 publications

References 24 publications

Systematic methodology for exploration of performance – Energy trade-offs in network applications using Dynamic Data Type refinement

Systematic methodology for exploration of performance – Energy trade-offs in network applications using Dynamic Data Type refinement

Energy-efficient dynamic memory allocators at the middleware level of embedded systems

Dynamic data type refinement methodology for systematic performance-energy design exploration of network applications

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