Center for Computing Research (CCR)
Center for Computing Research (CCR)

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An Accurate, Error-Tolerant, and Energy-Efficient Neural Network Inference Engine Based on SONOS Analog Memory

IEEE Transactions on Circuits and Systems I: Regular Papers

Xiao, T.P.; Feinberg, Benjamin M.; Bennett, Christopher H.; Agrawal, Vineet; Saxena, Prashant; Prabhakar, Venkatraman; Ramkumar, Krishnaswamy; Medu, Harsha; Raghavan, Vijay; Chettuvetty, Ramesh; Agarwal, Sapan; Marinella, Matthew

We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-To-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a > 10× gain in energy efficiency over state-of-The-Art digital and analog inference accelerators.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

Structural and Multidisciplinary Optimization

Bays, Nathan R.; Wildey, Timothy; Furlan, John M.; Krishnan, Pagalthivarthi; Visintainer, Robert J.; Mccann, Scott

High-fidelity complex engineering simulations are often predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of computational burden is usually enabled through parallelism in high-performance cluster (HPC) architecture. Optimization problems associated with these applications is a challenging problem due to the high computational cost of the high-fidelity simulations. In this paper, an asynchronous parallel constrained Bayesian optimization method is proposed to efficiently solve the computationally expensive simulation-based optimization problems on the HPC platform, with a budgeted computational resource, where the maximum number of simulations is a constant. The advantage of this method are three-fold. First, the efficiency of the Bayesian optimization is improved, where multiple input locations are evaluated parallel in an asynchronous manner to accelerate the optimization convergence with respect to physical runtime. This efficiency feature is further improved so that when each of the inputs is finished, another input is queried without waiting for the whole batch to complete. Second, the proposed method can handle both known and unknown constraints. Third, the proposed method samples several acquisition functions based on their rewards using a modified GP-Hedge scheme. The proposed framework is termed aphBO-2GP-3B, which means asynchronous parallel hedge Bayesian optimization with two Gaussian processes and three batches. The numerical performance of the proposed framework aphBO-2GP-3B is comprehensively benchmarked using 16 numerical examples, compared against other 6 parallel Bayesian optimization variants and 1 parallel Monte Carlo as a baseline, and demonstrated using two real-world high-fidelity expensive industrial applications. The first engineering application is based on finite element analysis (FEA) and the second one is based on computational fluid dynamics (CFD) simulations.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Quantitative Performance Assessment of Proxy Apps and Parents (Report for ECP Proxy App Project Milestone ADCD-504-28)

Cook, Jeanine; Aaziz, Omar R.; Chen, Si; Godoy, William; Powell, Amy J.; Watson, Gregory; Vaughan, Courtenay T.; Wildani, Avani

The ECP Proxy Application Project has an annual milestone to assess the state of ECP proxy applications and their role in the overall ECP ecosystem. Our FY22 March/April milestone (ADCD- 504-28) proposed to: Assess the fidelity of proxy applications compared to their respective parents in terms of kernel and I/O behavior, and predictability. Similarity techniques will be applied for quantitative comparison of proxy/parent kernel behavior. MACSio evaluation will continue and support for OpenPMD backends will be explored. The execution time predictability of proxy apps with respect to their parents will be explored through a carefully designed scaling study and code comparisons. Note that in this FY, we also have quantitative assessment milestones that are due in September and are, therefore, not included in the description above or in this report. Another report on these deliverables will be generated and submitted upon completion of these milestones. To satisfy this milestone, the following specific tasks were completed: Study the ability of MACSio to represent I/O workloads of adaptive mesh codes. Re-define the performance counter groups for contemporary Intel and IBM platforms to better match specific hardware components and to better align across platforms (make cross-platform comparison more accurate). Perform cosine similarity study based on the new performance counter groups on the Intel and IBM P9 platforms. Perform detailed analysis of performance counter data to accurately average and align the data to maintain phases across all executions and develop methods to reduce the set of collected performance counters used in cosine similarity analysis. Apply a quantitative similarity comparison between proxy and parent CPU kernels. Perform scaling studies to understand the accuracy of predictability of the parent performance using its respective proxy application. This report presents highlights of these efforts.

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TYPE Other Report YEAR 2022
DOIOSTI

Inverse Methods - Users Manual 5.6

Walsh, Timothy; Akcelik, Volkan; Aquino, Wilkins; Mccormick, Cameron; Sanders, Clay M.; Treweek, Benjamin; Kurzawski, Andrew J.; Smith, Chandler B.

The inverse methods team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verfication/validation analysis of weapons systems. This document provides a user's guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.

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TYPE SAND Report YEAR 2022
DOIOSTI

Self-Induced Curvature in an Internally Loaded Peridynamic Fiber

Silling, Stewart

A straight fiber with nonlocal forces that are independent of bond strain is considered. These internal loads can either stabilize or destabilize the straight configuration. Transverse waves with long wavelength have unstable dispersion properties for certain combinations of nonlocal kernels and internal loads. When these unstable waves occur, deformation of the straight fiber into a circular arc can lower its potential energy in equilibrium. The equilibrium value of the radius of curvature is computed explicitly.

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TYPE SAND Report YEAR 2022
DOIOSTI
Results 1451–1500 of 12,511
Results 1451–1500 of 12,511
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