Approximation-Conscious IC Testing

Masadeh, Mahmoud; Hasan, Osman; Tahar, Sofiène

doi:10.1109/icm.2018.8704099

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Previously, various works proposed approximate ICs by designing exact ICs and accepting the defective with minimal fault coverage as approximate ICs [25]. Others proposed designing approximate ICs, and accepting a defective approximate IC if the manufacturing error is within the acceptable approximation error [4]. However, the proposed chips were 2D, not 3D and the approximation is for the logic while considering the interconnect as fault-free.…”

Section: Related Workmentioning

confidence: 99%

“…3D-SICs is a continuation of Moore's Law, which is called more than Moore's law. This design paradigm delivers various benefits such as reduced power consumption, reduced footprint, high bandwidth communication, low latency between dies, high transistor density per volume unit, and heterogeneous (e.g., logic, memory, radio frequency (RF) circuits, analog circuits, and sensors) integration [4].…”

Section: Introductionmentioning

confidence: 99%

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Approximate TSV-based 3D Stacked Integrated Circuits by Inexact Interconnects

Masadeh¹

2022

IJACSA

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Three-Dimensional Stacked Integrated Circuit (3D-SICs) based on Through-Silicon Vias (TSVs) provide a highdensity integration technology. However, integrating pre-tested dies requires post-bond interconnect testing, which is complex and costly. An imperfect TSV-based interconnect indicates a defective chip that should be rejected. Thus, it increases the yield loss and test cost. On the other hand, approximate computing (AC) is a promising design paradigm suitable for errorresilient applications, e.g., processing sensory-generated data, by judiciously sacrificing output accuracy. AC perform inexact operations and accepts inexact data. Thus, introducing AC into 3D-SICs will significantly ameliorate the efficiency of design approximation. Therefore, this work aims to increase the yield and reduce the test cost by accepting 3D-SICs with defected interconnects as approximate 3D-SICs. This work considers 3D-SICs, where the sensor is stacked on logic (CPU) which is stacked on memory (DRAM). Then, use the memory-based interconnect testing (MBIT) approach to detect and diagnose the faulty interconnect. Based on the detected fault location and type, and for a maximum allowed error, some sensory 3D-SICs with defected LSBs interconnects are accepted and used in errorresilient and data-intensive applications. Targeting data lines only, 50% of the defected interconnects, i.e., least significant bits (LSBs), were accepted as approximate. Thus, the proposed work was able to significantly increase the yield. Two applications, i.e., ECG signal compression and detecting of their R peaks, demonstrated the effectiveness of using a sensory device with a faulty data line in its least significant 8-bits. The approximate ECG signals have a compression rate higher than the exact with negligible (around 0.1%) reduced accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Approximate TSV-based 3D Stacked Integrated Circuits by Inexact Interconnects

Masadeh¹

2022

IJACSA

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“…Thus, the ability to offer a prioritized hospitalization for severe cases is crucial, where the inability to do so exacerbates the plague rate and significantly hinders disease containment [13]. To help the medical systems, especially in the developed countries with limited resources, various prediction models have been introduced for fast, cheap, and reliable diagnosis of COVID-19 [14].…”

Section: Introductionmentioning

confidence: 99%

An efficient machine learning-based COVID-19 identification utilizing chest X-ray images

Masadeh

AlShorman

et al. 2022

IJ-AI

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There is no well-known vaccine for coronavirus disease (COVID-19) with 100% efficiency. COVID-19 patients suffer from a lung infection, where lung-related problems can be effectively diagnosed with image techniques. The golden test for COVID-19 diagnosis is the RT-PCR test, which is costly, time-consuming and unavailable for various countries. Thus, machine learning-based tools are a viable solution. Here, we used a labelled chest X-ray of three categories, then performed data cleaning and augmentation to use the data in deep learning-based convolutional neural network (CNN) models. We compared the performance of different models that we gradually built and analyzed their accuracy. For that, we used 2905 chest X-ray scan samples. We were able to develop a model with the best accuracy of 97.44% for identifying COVID-19 using X-ray images. Thus, in this paper, we attested the feasibility of efficiently applying machine learning (ML) based models for medical image classification.

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“…Despite the unprecedented power saving and reduced execution time introduced by design approximation, it is still an immature computing paradigm [8], where to the best of our knowledge, a formal model of the impact of approximation on accuracy metric is still missing [9]. However, accuracy performance of approximate designs is highly input-dependent [10], where we know relatively little about enhancing the accuracy of approximation in a disciplined manner.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Self-Compensating Approximate Computing

Masadeh¹,

Hasan²,

Tahar³

2020

Preprint

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Dedicated hardware accelerators are suitable for parallel computational tasks. Moreover, they have the tendency to accept inexact results. These hardware accelerators are extensively used in image processing and computer vision applications, e.g., to process the dense 3-D maps required for self-driving cars. Such error-tolerant hardware accelerators can be designed approximately for reduced power consumption and/or processing time. However, since for some inputs the output errors may reach unacceptable levels, the main challenge is to enhance the accuracy of the results of approximate accelerators and keep the error magnitude within an allowed range. Towards this goal, in this paper, we propose a novel machine learning-based self-compensating approximate accelerators for energy efficient systems. The proposed error compensation module, which is integrated within the architecture of approximate hardware accelerators, efficiently reduces the accumulated error at its output. It utilizes lightweight supervised machine learning techniques, i.e., decision tree, to capture input dependency of the error. We consider image blending application in multiplication mode to demonstrate a practical application of self-compensating approximate computing. Simulation results show that the proposed design of self-compensating approximate accelerator can achieve about 9% accuracy enhancement, with negligible overhead in other performance measures, i.e., power, area, delay and energy.

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Approximation-Conscious IC Testing

Cited by 6 publications

References 13 publications

Approximate TSV-based 3D Stacked Integrated Circuits by Inexact Interconnects

Approximate TSV-based 3D Stacked Integrated Circuits by Inexact Interconnects

An efficient machine learning-based COVID-19 identification utilizing chest X-ray images

Machine Learning-Based Self-Compensating Approximate Computing

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