Implementing fast and accurate Support Vector Machine (SVM) classifiers in embedded systems with limited compute and memory capacity and in applications with real-time constraints, such as continuous medical monitoring for anomaly detection, can be challenging and calls for low cost, low power and resource efficient hardware accelerators. In this paper, we propose a flexible FPGA-based SVM accelerator highly optimized through a dataflow architecture. Thanks to High Level Synthesis (HLS) and the dataflow method, our design is scalable and can be used for large data dimensions when there is limited on-chip memory. The hardware parallelism is adjustable and can be specified according to the available FPGA resources. The performance of different SVM kernels are evaluated in hardware. In addition, an efficient fixed-point implementation is proposed to improve the speed. We compared our design with recent SVM accelerators and achieved a minimum of 10x speed-up compared to other HLS-based and 4.4x compared to HDL-based designs.

HLS-based dataflow hardware architecture for Support Vector Machine in FPGA / Mansoori, Mohammad Amir; Casu, Mario R.. - ELETTRONICO. - (2022), pp. 41-45. ((Intervento presentato al convegno 2022 IEEE International Symposium on Circuits and Systems (ISCAS) tenutosi a Austin, Texas, USA nel 27 May 2022 - 01 June 2022 [10.1109/ISCAS48785.2022.9937927].

HLS-based dataflow hardware architecture for Support Vector Machine in FPGA

Mansoori, Mohammad Amir;Casu, Mario R.
2022

Abstract

Implementing fast and accurate Support Vector Machine (SVM) classifiers in embedded systems with limited compute and memory capacity and in applications with real-time constraints, such as continuous medical monitoring for anomaly detection, can be challenging and calls for low cost, low power and resource efficient hardware accelerators. In this paper, we propose a flexible FPGA-based SVM accelerator highly optimized through a dataflow architecture. Thanks to High Level Synthesis (HLS) and the dataflow method, our design is scalable and can be used for large data dimensions when there is limited on-chip memory. The hardware parallelism is adjustable and can be specified according to the available FPGA resources. The performance of different SVM kernels are evaluated in hardware. In addition, an efficient fixed-point implementation is proposed to improve the speed. We compared our design with recent SVM accelerators and achieved a minimum of 10x speed-up compared to other HLS-based and 4.4x compared to HDL-based designs.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2973048