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비선형 함수 연산을 위한 FPGA 기반의 부동 소수점 프로세서의 설계

Design of a Floating Point Processor for Nonlinear Functions on an Embedded FPGA

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Abstract

This paper presents the hardware design of a 32bit floating point based processor. The processor can perform nonlinear functions such as sinusoidal functions, exponential functions, and other mathematical functions. Using the Taylor series and Newton - Raphson method, nonlinear functions are approximated. The processor is actually embedded on an FPGA chip and tested. The numerical accuracy of the functions is compared with those computed by the MATLAB and confirmed the performance of the processor.

참고문헌 (5)

  1. Seul, Jung, "Hardware Implementation of a Real-Time Neural Network Controller With a DSP and and FPGA for Nonlinear Systems", IEEE Transaction on Industrial Electronics, 54(1), pp.265~271 (2007)
  2. S. Himavathi, "Feedforward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilization", IEEE Transactions on Neural Network, 18(3), pp.880~888 (2007)
  3. Pedro C. Diniz, "Design of a Field-Programmable Dual-Precision Floating-Point Arithmetic Unit", International Conference on Field Programmable Logic and Applications, pp.1~4 (2006)
  4. Martin J. Pearson, "Implementing Spiking Neural Networks for Real-Time Signal-Processing and Control Applications: A Model-Validated FPGA Approach", IEEE Transactions on Neural Network, 18(5), pp.1472~1487 (2007)
  5. E. Pasero, "Hw-Sw Codesign of a Flexible Neural Controller Through a FPGA-based Neural Network Programmed in VHDL", IEEE International Joint Conference on Neural Networks, 4, pp.3161~3165 (2007)

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