Low-area and accurate inner product and digital filters based on stochastic computing

Abdellatef, Hamdan; Khalil-Hani, Mohamed; Shaikh-Husin, Nasir; Ayat, Sayed Omid

doi:10.1016/j.sigpro.2021.108040

Cited by 10 publications

(2 citation statements)

References 11 publications

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“…Digital filters can be designed in many ways [ 15 , 16 , 17 , 18 ]; the simplest and most widely used method used method for designing a linear phase response digital filter is via the Fourier series method using a window function [ 19 ]. When the frequency of the measured signal is known, the frequency of the measured signal is taken as the center frequency of the filter passband.…”

Section: Signal Processing Algorithm Designmentioning

confidence: 99%

Design and Implementation of a Subnanometer Heterodyne Interference Signal Processing Algorithm with a Dynamic Filter

Zeng

Zhao

Xiong

et al. 2022

Sensors

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In this study, a subnanometer heterodyne interference signal processing algorithm with a dynamic filter is proposed. The algorithm can effectively reduce the measurement error caused by the noise introduced in the optical path and circuit. Because of the low signal−to−noise ratio of the measurement signal, a dynamic filter with variable coefficients is designed. The role of the bi−quadrature lock−in amplifier algorithm in the problem of different amplitudes among the measurement signal, reference signal, and uncertainty of the frequency difference of the dual−frequency laser is analyzed. With the aid of the heterodyne interferometry platform, the error in the solution results of the proposed algorithm and the conventional algorithm is compared. The results indicate that the maximum deviation of the phase increment of the algorithm does not exceed 6 mrad, the single−cycle phase difference can be subdivided by 1024, and the system resolution reaches 0.15 nm.

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Section: Signal Processing Algorithm Designmentioning

confidence: 99%

Design and Implementation of a Subnanometer Heterodyne Interference Signal Processing Algorithm with a Dynamic Filter

Zeng

Zhao

Xiong

et al. 2022

Sensors

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show abstract

“…In contrast to previous approaches to hardware implementation of Izhikevich neurons, our proposal advocates for the utilization of stochastic computing (SC), an unconventional computing technique that has demonstrated effectiveness in specific applications [29][30][31][32][33][34][35][36]. It processes information using probabilities instead of conventional binary or real-valued representations [37].…”

Section: Introductionmentioning

confidence: 99%

SC-IZ: A Low-Cost Biologically Plausible Izhikevich Neuron for Large-Scale Neuromorphic Systems Using Stochastic Computing

Liu,

Xiao,

et al. 2024

Electronics

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Neurons are crucial components of neural networks, but implementing biologically accurate neuron models in hardware is challenging due to their nonlinearity and time variance. This paper introduces the SC-IZ neuron model, a low-cost digital implementation of the Izhikevich neuron model designed for large-scale neuromorphic systems using stochastic computing (SC). Simulation results show that SC-IZ can reproduce the behaviors of the original Izhikevich neuron. The model is synthesized and implemented on an FPGA. Comparative analysis shows improved hardware efficiency; reduced resource utilization, which is a 56.25% reduction in slices, 57.61% reduction in Look-Up Table (LUT) usage, and a 58.80% reduction in Flip-Flop (FF) utilization; and a higher operating frequency compared to state-of-the-art Izhikevich implementation.

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Survey of Stochastic Number Generators and Optimizing Techniques

Nahar

Khandekar

Deshmukh

et al. 2023

Lecture Notes in Electrical Engineering

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Low-area and accurate inner product and digital filters based on stochastic computing

Cited by 10 publications

References 11 publications

Design and Implementation of a Subnanometer Heterodyne Interference Signal Processing Algorithm with a Dynamic Filter

Design and Implementation of a Subnanometer Heterodyne Interference Signal Processing Algorithm with a Dynamic Filter

SC-IZ: A Low-Cost Biologically Plausible Izhikevich Neuron for Large-Scale Neuromorphic Systems Using Stochastic Computing

Survey of Stochastic Number Generators and Optimizing Techniques

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