More Like this

1. A library based on deep neural networks for modeling the degradation of FinFET SRAM performance metrics due to aging

   https://doi.org/10.1016/j.microrel.2019.113486  

   Zhang, R.; Liu, Z.; Yang, K.; Liu, T.; Cai, W.; MIlor, L. (September 2019, Microelectronics reliability)

   null (Ed.)

   FinFET SRAM cells suffer from front-end wearout mechanisms, such as bias temperature instability and hot carrier injection. In this paper, we built a library based on deep neural networks (DNNs) to speed up the process of simulating FinFET SRAM cells' degradation. This library consists of two parts. The first part calculates circuit configuration parameters, wearout parameters, and the other input variables for the DNN. The second part calls for the DNN to determine the shifted circuit performance metrics. A DNN with more than 99% accuracy is achieved with training data from standard Hspice simulations. The correctness of the DNN is also validated in the presence of input variations. With this library, the simulation speed is one hundred times faster than Hspice simulations. We can display the cell's degradation under various configurations easily and quickly. Also, the DNN-based library can help protect intellectual property without showing users the circuit's details. 

   more » « less
2. Metallicities of 20 Million Giant Stars Based on Gaia XP Spectra

   https://doi.org/10.3847/1538-4365/add5e3  

   Yang_杨, Lin 琳; Yuan_苑, Haibo 海波; Huang_黄, Bowen 博闻; Zhang_张, Ruoyi 若羿; Beers, Timothy C; Xiao_肖, Kai 凯; Xu_徐, Shuai 帅; Huang_黄, Yang 样; Xiang_向, Maosheng 茂盛; Zhang_张, Meng 萌; et al (June 2025, The Astrophysical Journal Supplement Series)

   Abstract We design an uncertainty-aware cost-sensitive neural network (UA-CSNet) to estimate metallicities from dereddened and corrected Gaia BP/RP (XP) spectra for giant stars. This method accounts for both stochastic errors in the input spectra and the imbalanced density distribution in [Fe/H] values. With a specialized architecture and training strategy, the UA-CSNet improves the precision of the predicted metallicities, especially for very metal-poor (VMP; [Fe/H] ≤ −2.0) stars. With the PASTEL catalog as the training sample, our model can estimate metallicities down to [Fe/H] ∼ −4. We compare our estimates with a number of external catalogs and conduct tests using star clusters, finding overall good agreement. We also confirm that our estimates for VMP stars are unaffected by carbon enhancement. Applying the UA-CSNet, we obtain reliable and precise metallicity estimates for approximately 20 million giant stars, including 360,000 VMP stars and 50,000 extremely metal-poor ([Fe/H] ≤ −3.0) stars. The resulting catalog is publicly available via the Chinese Virtual Observatory at doi: 10.12149/101604. This work highlights the potential of low-resolution spectra for metallicity estimation and provides a valuable data set for studying the formation and chemodynamical evolution of our Galaxy. 

   more » « less
3. Fast Automatic Parameter Selection for MRI Reconstruction

   https://doi.org/10.1109/ISBI45749.2020.9098569  

   Toma, Tanjin Taher; Weller, Daniel S. (April 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI))

   This paper proposes an automatic parameter selection framework for optimizing the performance of parameter-dependent regularized reconstruction algorithms. The proposed approach exploits a convolutional neural network for direct estimation of the regularization parameters from the acquired imaging data. This method can provide very reliable parameter estimates in a computationally efficient way. The effectiveness of the proposed approach is verified on transform-learning-based magnetic resonance image reconstructions of two different publicly available datasets. This experiment qualitatively and quantitatively measures improvement in image reconstruction quality using the proposed parameter selection strategy versus both existing parameter selection solutions and a fully deep-learning reconstruction with limited training data. Based on the experimental results, the proposed method improves average reconstructed image peak signal-to-noise ratio by a dB or more versus all competing methods in both brain and knee datasets, over a range of subsampling factors and input noise levels. 

   more » « less
4. Analyzing and mitigating data stalls in DNN training

   https://doi.org/10.14778/3446095.3446100  

   Mohan, Jayashree; Phanishayee, Amar; Raniwala, Ashish; Chidambaram, Vijay (January 2021, Proceedings of the VLDB Endowment)

   null (Ed.)

   Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While prior research has explored many different ways of reducing DNN training time, the impact of input data pipeline , i.e., fetching raw data items from storage and performing data pre-processing in memory, has been relatively unexplored. This paper makes the following contributions: (1) We present the first comprehensive analysis of how the input data pipeline affects the training time of widely-used computer vision and audio Deep Neural Networks (DNNs), that typically involve complex data pre-processing. We analyze nine different models across three tasks and four datasets while varying factors such as the amount of memory, number of CPU threads, storage device, GPU generation etc on servers that are a part of a large production cluster at Microsoft. We find that in many cases, DNN training time is dominated by data stall time : time spent waiting for data to be fetched and pre-processed. (2) We build a tool, DS-Analyzer to precisely measure data stalls using a differential technique, and perform predictive what-if analysis on data stalls. (3) Finally, based on the insights from our analysis, we design and implement three simple but effective techniques in a data-loading library, CoorDL, to mitigate data stalls. Our experiments on a range of DNN tasks, models, datasets, and hardware configs show that when PyTorch uses CoorDL instead of the state-of-the-art DALI data loading library, DNN training time is reduced significantly (by as much as 5X on a single server). 

   more » « less
5. Augmentation of a Lyapunov-Based Deep Neural Network Controller with Concurrent Learning for Control-Affine Nonlinear Systems

   https://doi.org/10.23919/ACC60939.2024.10644820  

   Basyal, Sujata; Ting, Jonathan; Mishra, Kislaya; Allen, Brendon C (July 2024, IEEE)

   A deep neural network (DNN)-based adaptive controller with a real-time and concurrent learning (CL)-based adaptive update law is developed for a class of uncertain, nonlinear dynamic systems. The DNN in the control law is used to approximate the uncertain nonlinear dynamic model. The inner-layer weights of the DNN are updated offline using data collected in real-time; whereas, the output-layer DNN weights are updated online (i.e., in real-time) using the Lyapunov- and CL-based adaptation law. Specifically, the inner-layer weights of the DNN are trained offline (concurrent to real-time execution) after a sufficient amount of data is collected in real-time to improve the performance of the system, and after training is completed the inner-layer DNN weights are updated in batch-updates. The key development in this work is that the output-layer DNN update law is augmented with CL-based terms to ensure that the output-layer DNN weight estimates converge to within a ball of their optimal values. A Lyapunov-based stability analysis is performed to ensure semi-global exponential convergence to an ultimate bound for the trajectory tracking errors and the output-layer DNN weight estimation errors. 

   more » « less