Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise

Yang, Xiaoxuan; Belakaria, Syrine; Joardar, Biresh Kumar; Yang, Huanrui; Doppa, Janardhan Rao; Pande, Partha Pratim; Chakrabarty, Krishnendu; Li, Hai

doi:10.1109/iccad51958.2021.9643444

Cited by 20 publications

(13 citation statements)

References 38 publications

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“…Its evaluation indexes TPR, TNR, and TR are 73.89%, 76.57%, and 75.05%, respectively, higher than the other six prediction models, which indicates that the model has better performance in enterprise financial risk forecasting. In addition, the better prediction performance of the models in [22] and in this study indicates that the multiobjective optimization algorithm outperforms the single-objective approach in terms of prediction effectiveness. e above experimental results analyzed the differences of the models only in terms of the numerical magnitude of the prediction accuracy and lacked mathematical and statistical significance.…”

Section: Selection Of Feature Indicatorsmentioning

confidence: 60%

“…e KMO and Bartlett's spherical tests are performed before PCA to determine whether each indicator is suitable for principal component analysis. e test results show that the KMO value of financial risk forecast indicators is 0.65, and the p value of Bartlett spherical is significant, indicating a relatively obvious correlation between the ere are six types of comparison models, including BP neural network (BPNN), SVM, long short-term memory network (LSTM), differential evolutionary algorithm-LSTM (DE-LSTM), simulated annealing-LSTM (SA-LSTM), and NSGA--II-DNN model in [22].…”

Section: Selection Of Feature Indicatorsmentioning

confidence: 99%

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A Deep Learning Model for ERP Enterprise Financial Management System

Zhang

2022

Advances in Multimedia

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With the advent of the information age, the need for information technology construction is beginning to be realized when working in corporate financial management. The application of ERP systems to financial management has become a major trend in the development of modern society. This can help companies collect financial information in real time and analyze and process the obtained information. This paper first gives the significance and models of the ERP financial management system. Then, a financial risk prediction model based on a deep learning model is designed. The method proposes an improved temporal convolutional network-long and short-term memory network (TCN_LSTM) structure and introduces an optimization algorithm to optimize the parameters of the deep learning model. Finally, several benchmark models and evaluation methods are used for comparative study. The experimental results show that the deep learning risk prediction model has significant superiority in prediction accuracy and stability. The proposed model can help enterprises organize their limited resources, realize the scientific allocation of enterprise resources, and create more benefits for enterprises.

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Section: Selection Of Feature Indicatorsmentioning

confidence: 60%

Section: Selection Of Feature Indicatorsmentioning

confidence: 99%

A Deep Learning Model for ERP Enterprise Financial Management System

Zhang

2022

Advances in Multimedia

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“…Multiple memristor cells can be integrated to represent a single weight if the memristor cell's resolution is smaller than the weight's resolution, which is often the case for a ReRAM-based PIM system. [42] Thus, programming noise that relates to the more significant cells will be multiplied by a scalar during integration, and the programming noise in the most significant cell is of the greatest concern. Long et al proposed a dynamic fixed-point representation to reduce the unused most significant bit of memristor cells.…”

Section: Circuit-level and Algorithm-level Solutionsmentioning

confidence: 99%

“…In recent work, Yang et al consider different existing noise sources in PIM systems and target at achieving robustness and effectiveness with minimal design exploration cost. [42] They design a ReRAM-based stochastic-noise-aware training method (ReSNA), and include three major design analyses. First, the distribution of noise under frequency and temperature settings is analyzed.…”

Section: System-level Solutionsmentioning

confidence: 99%

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Tolerating Noise Effects in Processing‐in‐Memory Systems for Neural Networks: A Hardware–Software Codesign Perspective

Yang

et al. 2022

Advanced Intelligent Systems

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Neural networks have been widely used for advanced tasks from image recognition to natural language processing. Many recent works focus on improving the efficiency of executing neural networks in diverse applications. Researchers have advocated processing‐in‐memory (PIM) architecture as a promising candidate for training and testing neural networks because PIM design can reduce the communication cost between storage and computing units. However, there exist noises in the PIM system generated from the intrinsic physical properties of both memory devices and the peripheral circuits. The noises introduce challenges in stably training the systems and achieving high test performance, e.g., accuracy in classification tasks. This review discusses the current approaches to tolerating noise effects for both training and inference in PIM systems and provides an analysis from a hardware–software codesign perspective. Noise‐tolerant strategies for PIM systems based on resistive random‐access memory (ReRAM), including circuit‐level, algorithm‐level, and system‐level solutions are explained. In addition, we also present some selected noise‐tolerate cases in PIM systems for generative adversarial networks and physical neural networks.

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