DL-RSIM: A Reliability and Deployment Strategy Simulation Framework for ReRAM-based CNN Accelerators

Lin, Wei‐Ting; Cheng, Hsiang-Yun; Yang, Chia-Lin; Lin, Meng-Yao; Lien, Kai; Hu, Hanwen; Chang, Hung-Sheng; Li, Hsiang-Pang; Chang, Meng‐Fan; Tsou, Yen-Ting; Nien, Chin-Fu

doi:10.1145/3507639

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…Another study uses a support vector machine (SVM)-based algorithm to solve an optimization problem of structure reliability. Other RAMS applications apply machine learning to simulate possible scenarios and evaluate system reliability [30,31]. The vast majority of techniques, for both RAMS and PHM, are supervised learning methods; some studies have also used transfer learning and self-supervised learning.…”

Section: Machine Learning Methods Reviewmentioning

confidence: 99%

Machine Learning Applications for Reliability Engineering: A Review

Payette

Abdul-Nour

2023

Sustainability

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The treatment of big data as well as the rapid improvement in the speed of data processing are facilitated by the parallelization of computations, cloud computing as well as the increasing number of artificial intelligence techniques. These developments lead to the multiplication of applications and modeling techniques. Reliability engineering includes several research areas such as reliability, availability, maintainability, and safety (RAMS); prognostics and health management (PHM); and asset management (AM), aiming at the realization of the life cycle value. The expansion of artificial intelligence (AI) modeling techniques combined with the various research topics increases the difficulty of practitioners in identifying the appropriate methodologies and techniques applicable. The objective of this publication is to provide an overview of the different machine learning (ML) techniques from the perspective of traditional modeling techniques. Furthermore, it presents a methodology for data science application and how machine learning can be applied in each step. Then, it will demonstrate how ML techniques can be complementary to traditional approaches, and cases from the literature will be presented.

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Section: Machine Learning Methods Reviewmentioning

confidence: 99%

Machine Learning Applications for Reliability Engineering: A Review

Payette

Abdul-Nour

2023

Sustainability

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“…(2) Support for hardware performance simulation: DL-RSIM [12] and PytorX [13] mainly support the accuracy simulation considering memristor non-ideal factors and do not focus too much on hardware performance. CIM-SIM is a system-level simulator that proposes the instruction set architecture for PIM and only evaluates the latency (i.e., number of clock ticks) from the instruction and compilation level.…”

Section: Pim Modeling Toolsmentioning

confidence: 99%

MNSIM 2.0: A Behavior-Level Modeling Tool for Processing-In-Memory Architectures

Zhu

Sun

Xie

et al. 2023

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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In the age of Artificial Intelligence (AI), the huge data movements between memory and computing units become the bottleneck of von Neumann architectures, i.e., the "memory wall" problem. In order to tackle this challenge, Processing-In-Memory (PIM) architectures are proposed, which perform in-situ computations in memory and give alternative solutions to boost the computing energy efficiency and performance. Because of the large-scale Neural Network (NN) algorithm models and the huge hardware design space, various factors affect computing accuracy and performance, bringing the need for efficient PIM modeling and evaluation tools.In this work, we propose a behavior-level modeling tool, MNSIM 2.0, to model the performance of PIM architectures efficiently. At the hardware level, MNSIM 2.0 provides a hierarchical PIM modeling structure with flexible architecture configurability and components extensibility. Moreover, the first unified PIM memory array model is proposed for describing both digital and analog PIM. At the algorithm level, MNSIM 2.0 supports the PIM-based NN computing accuracy simulation considering various architecture and device parameters. A PIM-oriented NN model training and quantization flow is also integrated to improve the performance gain brought by PIM. At the scheduling level, MNSIM 2.0 adopts a universal scheduling description compatible with different scheduling strategies. Validation using fabricated PIM macros shows the relative modeling error rate of MNSIM 2.0 is 3.8 ∼ 5.5%. Case studies show that MNSIM 2.0 enables PIM design space explorations, influences analysis of device parameters, and architecture design insight discoveries.

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“…Figure 2 shows an overview of a memristor-based neuromorphic computing system as well as the matrix-vector multiplication (MVM) operation conducted on memristor crossbars. In particular, a memristor crossbar array of M × N is programmed to represent the neural network weights by tuning the conductances [1,18]. When applying a input voltage vector V = [V 1 , V 2 ,..., V M ] to the wordlines, the array can output a current vector I = [I 1 , I 2 ,..., I N ] according to Kirchhoff's laws.…”

Section: Background and Related Workmentioning

confidence: 99%

A Design Methodology for Fault-Tolerant Neuromorphic Computing Using Bayesian Neural Network

Gao,

Xie,

Wei

2023

Micromachines

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Memristor crossbar arrays are a promising platform for neuromorphic computing. In practical scenarios, the synapse weights represented by the memristors for the underlying system are subject to process variations, in which the programmed weight when read out for inference is no longer deterministic but a stochastic distribution. It is therefore highly desired to learn the weight distribution accounting for process variations, to ensure the same inference performance in memristor crossbar arrays as the design value. In this paper, we introduce a design methodology for fault-tolerant neuromorphic computing using a Bayesian neural network, which combines the variational Bayesian inference technique with a fault-aware variational posterior distribution. The proposed framework based on Bayesian inference incorporates the impacts of memristor deviations into algorithmic training, where the weight distributions of neural networks are optimized to accommodate uncertainties and minimize inference degradation. The experimental results confirm the capability of the proposed methodology to tolerate both process variations and noise, while achieving more robust computing in memristor crossbar arrays.

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DL-RSIM: A Reliability and Deployment Strategy Simulation Framework for ReRAM-based CNN Accelerators

Cited by 11 publications

References 24 publications

Machine Learning Applications for Reliability Engineering: A Review

Machine Learning Applications for Reliability Engineering: A Review

MNSIM 2.0: A Behavior-Level Modeling Tool for Processing-In-Memory Architectures

A Design Methodology for Fault-Tolerant Neuromorphic Computing Using Bayesian Neural Network

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