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HAPE: A high-level area-power estimation framework for FPGA-based accelerators

Abstract : Recent embedded applications are widely used in several industrial domains such as automotive and multimedia systems. These applications are critical and complex, involving more computing resources and therefore increasing the power consumption of the system. Although performance still remains an important design metric, power consumption has become a critical factor for several systems, particularly after the increasing complexity of recent System-on-Chip (SoC) designs. Consequently, the whole computing domain is being forced to switch from a focus on high performance computation to energy-efficient computation. In addition to the time-to-market challenge, designers need to estimate, rapidly and accurately, both area occupation and power consumption of complex and diverse applications. High-Level Synthesis (HLS) has been emerged as an attractive solution for designers to address this challenge in order to explore a large number of design points at a high-level of abstraction. In this paper, we target FPGA-based accelerators. We propose HAPE, a high-level framework based on analytic models for area and power estimation without requiring register-transfer level (RTL) implementations. This technique allows to estimate the required FPGA resources and the power consumption at the source code level. The proposed models also enable a fast design space exploration (DSE) with different trade-offs through HLS optimization pragmas, including loop unrolling, pipelining, array partitioning, etc. The accuracy of our proposed models is evaluated by using a variety of synthetic benchmarks. Estimated power results are compared to real board measurements. The area and power estimation results are less than 5% of error compared to RTL implementations.
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Mariem Makni, Smail Niar, Mouna Baklouti, Mohamed Abid. HAPE: A high-level area-power estimation framework for FPGA-based accelerators. Microprocessors and Microsystems: Embedded Hardware Design , Elsevier, 2018, 63, pp.11-27. ⟨10.1016/j.micpro.2018.08.004⟩. ⟨hal-03400521⟩



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