Generation of New Low-Complexity March Algorithms for Optimum Faults Detection in SRAM

Jidin, Auzani; Hussin, Razaidi; Fook, Lee Weng; Mispan, Mohd Syafiq; Zakaria, Nor Azura; Ying, Loh Wan; Zamin, Norshuhani

doi:10.1109/tcad.2022.3229281

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…These methods may either be categorised as graph cut (GC) or belief propagation (BP) methods. The BP method lowers the energy function by continuously sending signals from the current node to nearby nodes in the MRF network [31], [32]. Since [33]- [35]'s introduction of convolutional neural network (CNN) trained on tiny image patch pairs with known actual disparity, attention to a deep learning-based stereo vision system has grown significantly.…”

Section: Introductionmentioning

confidence: 99%

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

Abd Gani,

Miskon,

Hamzah

et al. 2024

Bulletin EEI

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Machine vision research began with a single-camera system, but these systems had various limitations from having just one point-of-view of the environment and no depth information, therefore stereo cameras were invented. This paper proposes a hybrid method of a stereo matching algorithm with the goal of generating an accurate disparity map critical for applications such as 3D surface reconstruction and robot navigation to name a few. Convolutional neural network (CNN) is utilised to generate the matching cost, which is then input into cost aggregation to increase accuracy with the help of a bilateral filter (BF). Winner-take-all (WTA) is used to generate the preliminary disparity map. An edge-preserving filter (EPF) is applied to that output based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line to eliminate these artefacts. The transform warps the input signal adaptively to allow linear 1D filtering. Due to the filter's resistance to high contrast and brightness, it is effective in refining and removing noise from the output image. Based on experimental research employing a Middlebury standard validation benchmark, this approach gives high accuracy with an average non-occluded error of 6.71% comparable to other published methods.

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Section: Introductionmentioning

confidence: 99%

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

Abd Gani,

Miskon,

Hamzah

et al. 2024

Bulletin EEI

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An in-Array Build-In Self-Test Scheme for Embedded SRAM Array

Wei,

Cui,

Zhang

2024

IEEE Trans. Circuits Syst. II

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MBIST Implementation and Evaluation in FPGA Based on Low-Complexity March Algorithms

Jidin,

Hussin,

Lee

et al. 2023

J CIRCUIT SYST COMP

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March algorithms are widely used in Memory Built-In Self-Test (MBIST) on-chip memory testing, providing linear test complexities that reduce the test time and cost. However, studies show that March algorithms with complexities lower than 18N have poor coverages of faults that have emerged with the advent of the nanometer process technologies and are more relevant to nowadays memories. New March AZ1 and March AZ2 algorithms, with 13N and 14N complexities, respectively, were introduced to provide optimum coverage of those faults and to produce a shorter test than an 18N-complexity test algorithm with a lesser area overhead, thus reducing chip manufacturing costs. This paper presents the implementation and validation of MBIST controllers that applied the March AZ1 and March AZ2 algorithms in a Field-Programmable Gate Array (FPGA) device. They were implemented in the Intel Max 10 DE10-Lite FPGA Development Board. A test generator was built in FPGA, as an alternative to the external tester, to provide test vectors required in initiating the test on the memory model using the implemented MBIST. The FPGA experimental tests demonstrated that they function correctly as the expected test sequences were observed. In addition, their fault detection abilities were also validated through tests on a fault-injected memory model, which shows that the implemented March AZ1 and March AZ2 provide 80.6% and 83.3% coverage of the intended faults, respectively, which outperform any other existing 14N-complexity March algorithms.

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Generation of New Low-Complexity March Algorithms for Optimum Faults Detection in SRAM

Cited by 4 publications

References 25 publications

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

An in-Array Build-In Self-Test Scheme for Embedded SRAM Array

MBIST Implementation and Evaluation in FPGA Based on Low-Complexity March Algorithms

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