A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective

Podobas, Artur; Sano, Kentaro; Matsuoka, Satoshi

doi:10.1109/access.2020.3012084

Cited by 91 publications

(36 citation statements)

References 177 publications

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“…For example, Liu et al explicitly identify the necessity to improve the programmability and productivity of CGRAs [11]. Podobas et al draw the same conclusion and encourage the research community to focus on more complex (real world) applications rather than studying simple and small CGRA kernels [12]. This work addresses both of these findings by describing the integration of the required CGRA tooling into a control engineering environment and by investigation scalability effects on tool runtime and CGRA performance.…”

Section: Related Workmentioning

confidence: 86%

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UltraSynth: Insights of a CGRA Integration into a Control Engineering Environment

Wolf

Engel

Ruschke

et al. 2021

J Sign Process Syst

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Coarse Grained Reconfigurable Arrays (CGRAs) or Architectures are a concept for hardware accelerators based on the idea of distributing workload over Processing Elements. These processors exploit instruction level parallelism, while being energy efficient due to their simplistic internal structure. However, the incorporation into a complete computing system raises severe challenges at the hardware and software level. This article evaluates a CGRA integrated into a control engineering environment targeting a Xilinx Zynq System on Chip (SoC) in detail. Besides the actual application execution performance, the practicability of the configuration toolchain is validated. Challenges of the real-world integration are discussed and practical insights are highlighted.

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Section: Related Workmentioning

confidence: 86%

“…Besides the presented work, [10] is one of the rare publications explicitly stating that floating-point operations are supported. More specifically, [12] reports that floatingpoint support is clearly under-represented. Less than 25% of the published CGRAs support floating-point computations.…”

Section: Requirements Of Real-world Applicationsmentioning

confidence: 99%

UltraSynth: Insights of a CGRA Integration into a Control Engineering Environment

Wolf

Engel

Ruschke

et al. 2021

J Sign Process Syst

View full text Add to dashboard Cite

show abstract

“…Reference [31] presents many CGRAs and categorizes them into different categories: early pioneering, modern, larges, and deep learning. The article includes plentiful information, and the authors also collect the statistics to let readers know the developing trend of CGRA comparing to GPU.…”

Section: Coarse-grained Cell Array Acceleratorsmentioning

confidence: 99%

Low-Power Ultra-Small Edge AI Accelerators for Image Recognition with Convolution Neural Networks: Analysis and Future Directions

2021

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Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications require meeting performance targets and resilience constraints due to the limited device area and hostile environments for operation. Numerous research articles have proposed the edge AI accelerator for satisfying the applications, but not all include full specifications. Most of them tend to compare the architecture with other existing CPUs, GPUs, or other reference research, which implies that the performance exposé of the articles are not comprehensive. Thus, this work lists the essential specifications of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. The actual performance, implementation, and productized examples of edge AI accelerators are released in this paper. We introduce the evaluation results showing the edge AI accelerator design trend about key performance metrics to guide designers. Last but not least, we give out the prospect of developing edge AI’s existing and future directions and trends, which will involve other technologies for future challenging constraints.

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“…The functional units' type and architecture, the interconnection architecture, and the level of reconfigurability define the CGRA. During the last years many different CGRA were proposed [8][9][10][11]. The following paragraphs present a brief overview of known CGRAs to contextualise the Versat CGRA.…”

Section: Coarse-grained Reconfigurable Architecturesmentioning

confidence: 99%

Coarse-Grained Reconfigurable Computing with the Versat Architecture

et al. 2021

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Reconfigurable computing architectures allow the adaptation of the underlying datapath to the algorithm. The granularity of the datapath elements and data width determines the granularity of the architecture and its programming flexibility. Coarse-grained architectures have shown the right balance between programmability and performance. This paper provides an overview of coarse-grained reconfigurable architectures and describes Versat, a Coarse-Grained Reconfigurable Array (CGRA) with self-generated partial reconfiguration, presented as a case study for better understanding these architectures. Unlike most of the existing approaches, which mainly use pre-compiled configurations, a Versat program can generate and apply myriads of on-the-fly configurations. Partial reconfiguration plays a central role in this approach, as it speeds up the generation of incrementally different configurations. The reconfigurable array has a complete graph topology, which yields unprecedented programmability, including assembly programming. Besides being useful for optimising programs, assembly programming is invaluable for working around post-silicon hardware, software, or compiler issues. Results on core area, frequency, power, and performance running different codes are presented and compared to other implementations.

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A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective

Abstract: This article is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

Cited by 91 publications

References 177 publications

UltraSynth: Insights of a CGRA Integration into a Control Engineering Environment

UltraSynth: Insights of a CGRA Integration into a Control Engineering Environment

Low-Power Ultra-Small Edge AI Accelerators for Image Recognition with Convolution Neural Networks: Analysis and Future Directions

Coarse-Grained Reconfigurable Computing with the Versat Architecture

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