Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators

Jiang, Weiwen; Lou, Qiuwen; Yan, Zheyu; Yang, Lei; Hu, Jingtong; Hu, Xiaobo Sharon; Shi, Yiyu

doi:10.1109/tc.2020.2991575

Cited by 59 publications

(51 citation statements)

References 35 publications

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“…Experimental results show that UAE offers a 2.49% higher accuracy than NACIM [6] with 1.2x of time consumption. By further increasing search complexity, UAE reaches 6.39% higher accuracy than NACIM with 2.5x of search time.…”

Section: Arxiv:210706871v1 [Csar] 6 Jul 2021mentioning

confidence: 99%

“…In this work, we model device uncertainties as a whole and use a Gaussian distribution to represent them [4,6]. We set the mean of the uncertainty distribution to be zero, its variation to be 0.04, and for each device, its uncertainty is independently distributed.…”

Section: Uncertainty Modeling 31 Uncertainty Modelmentioning

confidence: 99%

“…We also use two different distributional properties, one is the mean value of all accuracy data (mean) and the other is 95% minimum of the accuracy data (95). The mean value indicates how a model performs under the effect of device uncertainty in average circumstances and the 95% minimum shows the models' behavior in worst-case scenarios.We offer a comparison for different specifications of UAE and two baseline methods, quantNAS[14], a state-of-the-art NAS framework to search for the optimal quantized CNN and NACIM[6], another uncertainty aware searching framework for CiM-based accelerators. In each experiment, the NAS controller searches for 2000 different architectures (episodes) and the trainer trains each generated architecture for 15 epochs.The DNN models finally presented by each search framework are also evaluated by mean and 95% minimum value with their accuracy data collected by 10k Monte-Carol simulation.…”

mentioning

confidence: 99%

“…The device uncertainty-induced performance degradation has been studied from different perspectives, including device-level observations [25], architecture level analysis [6], and behavioral level explorations [21]. Finding suitable pairs of DNN models and hardware designs that can together offer both desirable hardware reliability and high inference accuracy requires great effort.…”

mentioning

confidence: 99%

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Uncertainty Modeling of Emerging Device based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search

Yan

Juan

et al. 2021

Proceedings of the 26th Asia and South Pacific Design Automation Conference

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Emerging device-based Computing-in-memory (CiM) has been proved to be a promising candidate for high-energy efficiency deep neural network (DNN) computations. However, most emerging devices suffer uncertainty issues, resulting in a difference between actual data stored and the weight value it is designed to be. This leads to an accuracy drop from trained models to actually deployed platforms. In this work, we offer a thorough analysis of the effect of such uncertainties-induced changes in DNN models. To reduce the impact of device uncertainties, we propose UAE, an uncertaintyaware Neural Architecture Search scheme to identify a DNN model that is both accurate and robust against device uncertainties.

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Section: Arxiv:210706871v1 [Csar] 6 Jul 2021mentioning

confidence: 99%

Section: Uncertainty Modeling 31 Uncertainty Modelmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Uncertainty Modeling of Emerging Device based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search

Yan

Juan

et al. 2021

Proceedings of the 26th Asia and South Pacific Design Automation Conference

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“…DNN Algorithm and Accelerator Co-exploration. Exploring the networks and the corresponding accelerators in a joint manner [1,31,39,40,50,92] has shown great potential towards maximizing both accuracy and efficiency. Recent works have extended NAS to jointly search DNN accelerators in addition to DNN structures.…”

Section: Related Workmentioning

confidence: 99%

RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms

Zhang¹,

Banta²,

Fu³

et al. 2021

Preprint

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There exists a gap in terms of the signals provided by pacemakers (i.e., intracardiac electrogram (EGM)) and the signals doctors use (i.e., 12-lead electrocardiogram (ECG)) to diagnose abnormal rhythms. Therefore, the former, even if remotely transmitted, are not sufficient for doctors to provide a precise diagnosis, let alone make a timely intervention. To close this gap and make a heuristic step towards real-time critical intervention in instant response to irregular and infrequent ventricular rhythms, we propose a new framework dubbed RT-RCG to automatically search for (1) efficient Deep Neural Network (DNN) structures and then (2) corresponding accelerators, to enable Real-Time and high-quality Reconstruction of ECG signals from EGM signals. Specifically, RT-RCG proposes a new DNN search space tailored for ECG reconstruction from EGM signals, and incorporates a differentiable acceleration search (DAS) engine to efficiently navigate over the large and discrete accelerator design space to generate optimized accelerators. Extensive experiments and ablation studies under various settings consistently validate the effectiveness of our RT-RCG. To the best of our knowledge, RT-RCG is the first to leverage neural architecture search (NAS) to simultaneously tackle both reconstruction efficacy and efficiency.

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On the Reliability of Computing-in-Memory Accelerators for Deep Neural Networks

Yan

Shi

2022

Springer Series in Reliability Engineering

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Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators

Cited by 59 publications

References 35 publications

Uncertainty Modeling of Emerging Device based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search

Uncertainty Modeling of Emerging Device based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search

RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms

On the Reliability of Computing-in-Memory Accelerators for Deep Neural Networks

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