Hierarchical memory size estimation for loop fusion and loop shifting in data-dominated applications

Hu, Qubo; Vandecappelle, Arnout; Palkovic, Martin; Kjeldsberg, Per Gunnar; Brockmeyer, Erik; Catthoor, Francky

doi:10.1145/1118299.1118442

Cited by 22 publications

(31 citation statements)

References 18 publications

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“…1). This is why, although previous models (e.g., [4], [8]) produce important dynamic energy savings as well, our model led to 20%-33% better savings than them.…”

Section: Resultsmentioning

confidence: 64%

“…Although the reduction of the dynamic energy consumption in hierarchical memory subsystems has been addressed in the past (e.g., [4], [8]), the provided CAD solutions were based on mainly heuristic explorations of the solution space, rather than on a formal methodology. At the same time, the reduction of the static energy in the memory subsystem is a research direction only more recently addressed (e.g., [9]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

System-level exploration tool for energy-awarememory management in the design of multidimensional signal processing systems

Balasa¹,

Luican²,

Zhu³

et al. 2009

2009 Asia and South Pacific Design Automation Conference

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-Many signal processing systems, particularly in the multimedia and telecom domains, are synthesized to execute data-dominated applications. In such systems, data transfer and storage have a significant impact on both the system performance and the major cost parameters -power consumption and chip area. This paper presents a software tool for system-level exploration, where several memory management tasks are addressed in a common theoretical framework. The tool can compute the minimum storage requirement of a given application and can produce the graph of storage variation during the code execution; it offers memory allocation and signal assignment solutions both for flat and hierarchical organizations and optimizes the dynamic energy consumption in the memory subsystem.

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“…1). This is why, although previous models (e.g., [4], [8]) produce important dynamic energy savings as well, our model led to 20%-33% better savings than them.…”

Section: Resultsmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

System-level exploration tool for energy-awarememory management in the design of multidimensional signal processing systems

Balasa¹,

Luican²,

Zhu³

et al. 2009

2009 Asia and South Pacific Design Automation Conference

View full text Add to dashboard Cite

show abstract

“…The energy-efficient assignment of signals to the on-and off-chip memories has been studied since the late nineties. These previous works focused on partitioning the signals from the application code into so-called copy candidates (since the on-chip memories were usually caches), and on the optimal selection and assignment of these to different layers into the memory hierarchy (32), (7), (18). For instance, Kandemir and Choudhary analyze and exploit the temporal locality by inserting local copies (21).…”

Section: Introductionmentioning

confidence: 99%

Power-Aware Memory Allocation for Embedded Data-Intensive Signal Processing Applications

Balasa¹,

Luican²,

Zhu³

et al. 2010

Data Storage

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Many signal processing systems, particularly in the multimedia and telecommunication domains, are synthesized to execute data-intensive applications: their cost related aspectsnamely power consumption and chip area -are heavily influenced, if not dominated, by the data access and storage aspects. This chapter presents a power-aware memory allocation methodology. Starting from the high-level behavioral specification of a given application, this framework performs the assignment of of the multidimensional signals to the memory layers -the on-chip scratch-pad memory and the off-chip main memory -the goal being the reduction of the dynamic energy consumption in the memory subsystem. Based on the assignment results, the framework subsequently performs the mapping of signals into the memory layers such that the overall amount of data storage be reduced. This software system yields a complete allocation solution: the exact storage amount on each memory layer, the mapping functions that determine the exact locations for any array element (scalar signal) in the specification, and, in addition, an estimation of the dynamic energy consumption in the memory subsystem.

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“…The typical assumption int A [7][4], B [9][5], C [11][6], D [13] [7] ; for ( i=0; i<=6; i++ ) // 1st loop nest for ( j=0; j<=3; j++ )…”

mentioning

confidence: 99%

“…Hu et al perform hierarchical memory size estimation, taking data reuse and memory hierarchy allocation into account [13]. Inplace mapping is not incorporated in the current version, but is indicated as part of future work.…”

mentioning

confidence: 99%

Computation of the minimum data storage and applications in memory management for multimedia signal processing

Luican

Zhu

Balasa

2008

ICA

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Abstract. The amount of the data storage in signal processing systems, whose behavior is described by loop-organized algorithmic specifications, has an important impact on the overall energy consumption, chip area, as well as system performance. This paper presents a methodology based on lattices [25] which can be used to address several memory management tasks for applications with high-level specifications, where the main data structures are multidimensional arrays. This methodology was used in the past for the exact computation of the minimum data storage in applications with procedural, affine specifications [2]. The paper discusses two applications of that technique in the memory management of data-dominated signal processing systems: (1) the evaluation of the impact of loop transformations on the data storage, and (2) the assessment and efficient implementation of models of mapping multidimensional signals into the physical memory.

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Hierarchical memory size estimation for loop fusion and loop shifting in data-dominated applications

Cited by 22 publications

References 18 publications

System-level exploration tool for energy-awarememory management in the design of multidimensional signal processing systems

System-level exploration tool for energy-awarememory management in the design of multidimensional signal processing systems

Power-Aware Memory Allocation for Embedded Data-Intensive Signal Processing Applications

Computation of the minimum data storage and applications in memory management for multimedia signal processing

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