A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

Sensors
Marco BassoliIlaria De Munari

Abstract

Recent research in wearable sensors have led to the development of an advanced platform capable of embedding complex algorithms such as machine learning algorithms, which are known to usually be resource-demanding. To address the need for high computational power, one solution is to design custom hardware platforms dedicated to the specific application by exploiting, for example, Field Programmable Gate Array (FPGA). Recently, model-based techniques and automatic code generation have been introduced in FPGA design. In this paper, a new model-based floating-point accumulation circuit is presented. The architecture is based on the state-of-the-art delayed buffering algorithm. This circuit was conceived to be exploited in order to compute the kernel function of a support vector machine. The implementation of the proposed model was carried out in Simulink, and simulation results showed that it had better performance in terms of speed and occupied area when compared to other solutions. To better evaluate its figure, a practical case of a polynomial kernel function was considered. Simulink and VHDL post-implementation timing simulations and measurements on FPGA confirmed the good results of the stand-alone accumulator.

References

Aug 17, 2016·Sensors·Christopher Moufawad El AchkarKamiar Aminian
Jan 24, 2019·Sensors·Soumya Prakash RanaSandra Dudley
May 1, 2019·International Journal of Environmental Research and Public Health·Heesook Son, Hyerang Kim
May 6, 2019·IEEE Transactions on Biomedical Circuits and Systems·Val MikosWing Lok Au

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Software Mentioned

Simulink
Quartus
HDL Coder
VDHL
Vivado IP
Altera
MATLAB
Altera Cyclone
Simulink IP
Vivado

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