作者:
Butola, RajatLi, YimingKola, Sekhar ReddyNational Yang Ming Chiao Tung University
Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation
The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan
In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SP...
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Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection...
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Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agricu...
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Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving...
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Approximate computing has emerged as a design alternative to enhance design efficiency by capitalizing on the inherent error resilience observed in numerous applications. Various error-resilient and compute-intensive ...
Approximate computing has emerged as a design alternative to enhance design efficiency by capitalizing on the inherent error resilience observed in numerous applications. Various error-resilient and compute-intensive applications, such as signal, image and video processing, computer vision, and supervised machine learning, necessitate dedicated hardware accelerators for mean squared error estimation during runtime. In these application domains, using efficient arithmetic operators, particularly a squarer unit, represents one of the most effective strategies for low-power design. This work introduces an approximate Radix- $$2^{m}$$ squarer unit, denoted as AxRSU- $$2^{m}$$ . The proposed squarer unit employs m-bit approximate encoders to execute operations on m-bit data concurrently. The AxRSU- $$2^{m}$$ under consideration explores encoders with m equal to 2 (AxRSU-4), 3 (AxRSU-8), and 4 (AxRSU-16). These approximate encoders exhibit low complexity and diminish the necessary partial products operating on m bits simultaneously, thereby substantially enhancing energy efficiency and reducing circuit area in the AxRSU- $$2^{m}$$ . To illustrate the trade-off between error and quality in the AxRSU- $$2^{m}$$ , we apply it to an SSD (sum squared difference) hardware accelerator designed for video processing, with a square-accumulate serving as a case study. Our findings reveal a novel Pareto front, presenting eight optimal AxRSU- $$2^{m}$$ solutions that achieve accuracy levels ranging from 3.76 to 75.53%. These solutions yield energy savings ranging from 46.20 to 95.57% and circuit area reductions ranging from 37.68 to 66.73%.
作者:
Butola, RajatLi, YimingKola, Sekhar ReddyNational Yang Ming Chiao Tung University
Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University
Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan
Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capt...
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We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN construct...
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In this study, we report the process variation effect (PVE) including the work function fluctuation (WKF) on the DC/AC characteristic fluctuation of stacked gate-all-around silicon complementary field-effect transisto...
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Recently, a-IGZO has advanced toward the next-generation electronics system because of its compatibility with complementary metal oxide semiconductor (CMOS) and back-end-of-line (BOEL) based systems. A systematic elec...
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Image captioning aims to generate a description of visual contents with natural language automatically. This is useful in several potential applications, such as image understanding and virtual assistants. With recent...
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