Rapid Prototyping and Verification of Hardware Modules Generated Using HLS

Caba, Julian; Cardoso, João M. P.; Rincón, Fernando; Dondo, Julio; López, Juan Carlos

doi:10.1007/978-3-319-78890-6_36

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…On top of that, this strategy allowed avoiding the need to tailor the C++ HLS specification to make it usable in different steps of the design process. For example, it is common to write variations of the model depending on whether functional (C simulation) or RTL verification (co-simulation) is taking place due to the fact that the former is based on an untimed model and the latter introduces timing requirements [46,47].…”

Section: Codermentioning

confidence: 99%

FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations

et al. 2020

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Remote-sensing platforms, such as Unmanned Aerial Vehicles, are characterized by limited power budget and low-bandwidth downlinks. Therefore, handling hyperspectral data in this context can jeopardize the operational time of the system. FPGAs have been traditionally regarded as the most power-efficient computing platforms. However, there is little experimental evidence to support this claim, which is especially critical since the actual behavior of the solutions based on reconfigurable technology is highly dependent on the type of application. In this work, a highly optimized implementation of an FPGA accelerator of the novel HyperLCA algorithm has been developed and thoughtfully analyzed in terms of performance and power efficiency. In this regard, a modification of the aforementioned lossy compression solution has also been proposed to be efficiently executed into FPGA devices using fixed-point arithmetic. Single and multi-core versions of the reconfigurable computing platforms are compared with three GPU-based implementations of the algorithm on as many NVIDIA computing boards: Jetson Nano, Jetson TX2 and Jetson Xavier NX. Results show that the single-core version of our FPGA-based solution fulfils the real-time requirements of a real-life hyperspectral application using a mid-range Xilinx Zynq-7000 SoC chip (XC7Z020-CLG484). Performance levels of the custom hardware accelerator are above the figures obtained by the Jetson Nano and TX2 boards, and power efficiency is higher for smaller sizes of the image block to be processed. To close the performance gap between our proposal and the Jetson Xavier NX, a multi-core version is proposed. The results demonstrate that a solution based on the use of various instances of the FPGA hardware compressor core achieves similar levels of performance than the state-of-the-art GPU, with better efficiency in terms of processed frames by watt.

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Section: Codermentioning

confidence: 99%

FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations

et al. 2020

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FPGA-Based Solution for On-Board Verification of Hardware Modules Using HLS

et al. 2020

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High-Level Synthesis (HLS) tools provide facilities for the development of specialized hardware accelerators (HWacc). However, the verification stage is still the longest phase in the development life-cycle. Unlike in the software industry, HLS tools lack testing frameworks that could cover the whole design flow, especially the on-board verification stage of the generated RTL. This work introduces a framework for on-board verification of HLS-based modules by using reconfigurable systems and Docker containers with the aim to automate the verification process and preserve a clean testing environment, making the testbed reusable across different stages of the design flow. Moreover, our solution features a mechanism to check timing requirements of the HWacc. We have applied our solution to the C-kernels of the CHStone Benchmark on a Zedboard, in which the on-board verification process has been accelerated up to four times.

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Rapid Prototyping and Verification of Hardware Modules Generated Using HLS

Cited by 2 publications

References 14 publications

FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations

FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations

FPGA-Based Solution for On-Board Verification of Hardware Modules Using HLS

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