An architecture-agnostic integer linear programming approach to CGRA mapping

Chin, Stephen; Anderson, Jason

doi:10.1145/3195970.3195986

Cited by 27 publications

(7 citation statements)

References 19 publications

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“…A solution adopted in many cases is to let the control flow managed by a host processor. But this reduces greatly the pos- Spatial mapping [23], [30], [31] GA [19] SA [32], [33] ILP [23], [34], [35] Temporal mapping [12], [16], [26], [36]- [40] SA [22] ILP [41] B&B [42] CP [43] SAT [17] SMT [44] Binding [14], [24], [28], [45]- [47] QEA [48] SA [30], [49], [50] ILP [15], [48] Scheduling [24], [28], [36], [46], [48], [50]- [52] ILP [15], [53] sibilities to use the CGRA and increases the communication overhead, loosing sometimes the benefit of the acceleration provided by the CGRA. Another approach is to provide the CGRA with extra hardware features to support the control flow.…”

Section: B Control-flow Mappingmentioning

confidence: 99%

Twenty Years of Automated Methods for Mapping Applications on CGRA

Martin

2022

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Coarse-GrainedReconfigurable Architectures (CGRAs) emerged about 30 years ago. The very first CGRAs were programmed manually. Fortunately, some compilation approaches appeared rapidly to automate the mapping process. Numerous surveys on these architectures exist. Other surveys also gather the tools and methods, but none of them focuses on the mapping process only. This paper focuses solely on automated methods and techniques for mapping applications on CGRA and covers the last two decades of research. This paper aims at providing the terminology, the problem formulation, and a classification of existing methods. The paper ends with research challenges and trends for the future.

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Section: B Control-flow Mappingmentioning

confidence: 99%

Twenty Years of Automated Methods for Mapping Applications on CGRA

Martin

2022

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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“…The second category of approaches solves the scheduling and binding problems as a whole. Hence, [2,3] uses exact methods, e.g. ILP-based algorithms, to find optimal results.…”

Section: Related Workmentioning

confidence: 99%

An Efficient and Flexible Stochastic CGRA Mapping Approach

Das

Martin

Peyret

et al. 2022

ACM Trans. Embed. Comput. Syst.

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Coarse-Grained Reconfigurable Array (CGRA) architectures are promising high-performance and power-efficient platforms. However, mapping applications efficiently on CGRA is a challenging task. This is known to be an NP complete problem. Hence, finding good mapping solutions for a given CGRA architecture within a reasonable time is complex. Additionally, finding scalability in compilation time and memory footprint for large heterogeneous CGRAs is also a well known problem. In this paper, we present a stochastic mapping approach that can efficiently explore the architecture space and allows finding best of solutions while having limited and steady use of memory footprint. Experimental results show that our compilation flow allows to reach performances with low-complexity CGRA architectures that are as good as those obtained with more complex ones thanks to the better exploration of the mapping solution space. Parameters considered in our experiments are: number of tiles, Register File (RF) size, number of load/store units, network topologies, etc. Our results demonstrate that high-quality compilation for a wide range of applications is possible within reasonable run-times. Experiments with several DSP benchmarks show that the best CGRA configuration from the architectural exploration surpasses an ultra low-power DSP optimized RISC-V CPU to achieve up to 15.28 × (with an average of 6 × and minimum of 3.4 ×) performance gain and 29.7 × (with an average of 13.5 × and minimum of 6.3 ×) energy gain with an area overhead of 1.5 × only.

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“…For the mapping problem of DFG and MRRG, maps such as [19] and [20] uses the 0-1 integer linear programming. Their objective is to evaluate the specified value for the objective function of all variables.…”

Section: Integrated Mappingmentioning

confidence: 99%

Cgra Modulo Scheduling for Achieving Better Performance and Increased Efficiency

Phani.T¹

2021

TURCOMAT

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Coarse-Grained Reconfigurable Architectures (CGRA) is an effective solution for speeding up computer-intensive activities due to its high energy efficiency and flexibility sacrifices. The timely implementation of CGRA loops was one of the hardest problems in the analysis. Modulo scheduling (MS) was productive in order to implement loops on CGRAs. The problem remains with current MS algorithms, namely to map large and irregular circuits to CGRAs over a fair period of compilation with restricted computational and high-performance routing tools. This is mainly due to an absence of awareness of major mapping limits and a time consuming approach to solving temporary and space-related mapping using CGRA buffer tools. It aims to boost the performance and robust compilation of the CGRA modulo planning algorithm. The problem with the CGRA MS is divided into time and space and the mechanisms between the two problems have to be reorganized. We have a detailed, systematic mapping fluid that addresses the algorithms of the time mapping problem with a powerful buffer algorithm and efficient connection and calculation limitations. We create a fast-stable algorithm for spatial mapping with a retransmission and rearrangement mechanism. With higher performance and quicker build-up time, our MS algorithm can map loops to CBGRA. The results show that, given the same compilation budget, our mapping algorithm results in a better rate for compilation. The performance of this method will be increased from 5% to 14%, better than the standard CGRA mapping algorithms available.

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An architecture-agnostic integer linear programming approach to CGRA mapping

Cited by 27 publications

References 19 publications

Twenty Years of Automated Methods for Mapping Applications on CGRA

Twenty Years of Automated Methods for Mapping Applications on CGRA

An Efficient and Flexible Stochastic CGRA Mapping Approach

Cgra Modulo Scheduling for Achieving Better Performance and Increased Efficiency

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