Principal Component Analysis (PCA) is a widely used approach for dimensionality reduction in image processing. In microwave imaging, for example, it is used as an intermediate step toward image reconstruction. An FPGA hardware implementation of PCA is highly beneficial, especially as an accelerator for a low-cost embedded environment. In this paper we propose a flexible PCA hardware accelerator that can be used for different input data dimensions and input precisions. In addition, it supports both floating-point and fixed-point arithmetic representations. The target hardware is a ZYNQ SoC. We used High Level Synthesis (HLS) to quickly explore the design space and so to find the best implementation for a given setting of the application parameters and given the characteristics of the target hardware. We show the impact on performance of different hardware optimization techniques enabled by HLS. The proposed method outperforms a similar state-of-the-art HLS design in terms of latency and resource usage.

HLS-Based Flexible Hardware Accelerator for PCA Algorithm on a Low-Cost ZYNQ SoC / Mansoori, Mohammadamir; Casu, Mario R.. - ELETTRONICO. - (2019), pp. 1-7. (Intervento presentato al convegno 2019 IEEE Nordic Circuits and Systems Conference, NORCAS 2019: NORCHIP and International Symposium of System-on-Chip, SoC 2019 tenutosi a Helsinki, Finland nel 29-30 October 2019) [10.1109/NORCHIP.2019.8906893].

HLS-Based Flexible Hardware Accelerator for PCA Algorithm on a Low-Cost ZYNQ SoC

Mansoori, Mohammadamir;Casu, Mario R.
2019

Abstract

Principal Component Analysis (PCA) is a widely used approach for dimensionality reduction in image processing. In microwave imaging, for example, it is used as an intermediate step toward image reconstruction. An FPGA hardware implementation of PCA is highly beneficial, especially as an accelerator for a low-cost embedded environment. In this paper we propose a flexible PCA hardware accelerator that can be used for different input data dimensions and input precisions. In addition, it supports both floating-point and fixed-point arithmetic representations. The target hardware is a ZYNQ SoC. We used High Level Synthesis (HLS) to quickly explore the design space and so to find the best implementation for a given setting of the application parameters and given the characteristics of the target hardware. We show the impact on performance of different hardware optimization techniques enabled by HLS. The proposed method outperforms a similar state-of-the-art HLS design in terms of latency and resource usage.
2019
978-1-7281-2769-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2779752