GenMap: A Genetic Algorithmic Approach for Optimizing Spatial Mapping of Coarse-Grained Reconfigurable Architectures

Kojima, Tetsuya; Doan, Nguyen Anh Vu; Amano, Hideharu

doi:10.1109/tvlsi.2020.3009225

Cited by 28 publications

(8 citation statements)

References 46 publications

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“…Spatial computation is very similar to FPGAs. Along with spatial computations that all CGRAs support, temporal computations allow to share in time the hardware resources leading to more flexibility, but are often criticized to reduce the energy efficiency [19].…”

Section: B Terminology and Definitionsmentioning

confidence: 99%

“…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%

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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 Terminology and Definitionsmentioning

confidence: 99%

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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“…Por exemplo, para um grafo com apenas 21 nodos foram necessários 16 minutos para encontrar a soluc ¸ão. Em [Kojima et al 2020] um algoritmo genético para o mapeamento dinâmico com modulo scheduling em CGRA (GenMap). Os 7 benchmarks utilizados foram de 12 à 45 nodos de tamanho e baseados em aplicac ¸ões de processamento de sinal e criptografia.…”

Section: Trabalhos Relacionadosunclassified

Algoritmos de Posicionamento e Roteamento baseados em Travessia de Grafo para Arquiteturas Reconfiguráveis de Grão Grosso (CGRA)

Canesche¹,

Ferreira²

2022

Anais Do XXXV Concurso De Teses E Dissertações (CTD 2022)

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O hardware reconfigurável é flexível, eficiente energeticamente e oferece alto desempenho para vários domínios de aplicação. Os FPGAs são a tecnologia reconfigurável mais utilizada, entretanto o mapeamento da aplicação é NP-Completo. Os CGRAs simplificam o desenvolvimento de aplicações para hardware reconfigurável e alteram a granularidade das operações do nível de bit para o nível de palavras (8-32 bits). Este trabalho apresenta avanços no estado da arte para o problema de mapeamento em CGRA propondo novos algoritmos de travessia de grafos. As soluções alcançadas são ordens de grandeza mais rápidas sem comprometer a qualidade. Os algoritmos propostos foram 91x mais rápidos com uma redução de 1.7x nos recursos de balanceamento em comparação com a abordagem com Simulated Annealing.

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“…As for different computing platforms, various mapping scheduling algorithms have been proposed [7][8][9][10][11][12][13][14][15] . Owing to the characteristics of GCGRA flexible configuration in Processing Element (PE), it attracted considerable attention.…”

Section: Introductionmentioning

confidence: 99%

“…Owing to the characteristics of GCGRA flexible configuration in Processing Element (PE), it attracted considerable attention. Several scheduling algorithms have been proposed based on GCGRA platforms [10][11][12][13] . However, the existing algorithms exhibit the following defects.…”

Section: Introductionmentioning

confidence: 99%

Loop Subgraph-Level Greedy Mapping Algorithm for Grid Coarse-Grained Reconfigurable Array

Chen¹,

Cheng

Han

et al. 2023

Tsinghua Sci. Technol.

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To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints, we propose a Loop Subgraph-Level Greedy Mapping (LSLGM) algorithm using parallelism and processing element fragmentation. Under the constraint of a reconfigurable array, the LSLGM algorithm schedules node from a ready queue to the current reconfigurable cell array block. After mapping a node, its successor's indegree value will be dynamically updated. If its successor's indegree is zero, it will be directly scheduled to the ready queue; otherwise, the predecessor must be dynamically checked. If the predecessor cannot be mapped, it will be scheduled to a blocking queue. To dynamically adjust the ready node scheduling order, the scheduling function is constructed by exploiting factors, such as node number, node level, and node dependency. Compared with the loop subgraph-level mapping algorithm, experimental results show that the total configuration cycle of the LSLGM algorithm decreases by an average of 32.9% (PEA 4 4 ) and 33.9% (PEA 7 7 ). Compared with the epimorphism map algorithm, the total cycles of the LSLGM algorithm decrease by an average of 38.4% (PEA 4 4 ) and 35.2% (PEA 7 7 ). The feasibility of LSLGM is verified.

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GenMap: A Genetic Algorithmic Approach for Optimizing Spatial Mapping of Coarse-Grained Reconfigurable Architectures

Cited by 28 publications

References 46 publications

Twenty Years of Automated Methods for Mapping Applications on CGRA

Twenty Years of Automated Methods for Mapping Applications on CGRA

Algoritmos de Posicionamento e Roteamento baseados em Travessia de Grafo para Arquiteturas Reconfiguráveis de Grão Grosso (CGRA)

Loop Subgraph-Level Greedy Mapping Algorithm for Grid Coarse-Grained Reconfigurable Array

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