Saibal Mukhopadhyay

TL;DR: In this article, the authors proposed an ensemble-based approach that quantifies uncertainty in predictions made by ML models of an HMD, when it encounters an unknown workload than the ones it was trained on.

TL;DR: This paper proposes a deep learning model that learns to predict unknown spatio-temporal dynamics using data from sparsely-distributed data sites using the radial basis function (RBF) collocation method which is often used for meshfree solution of partial differential equations.

TL;DR: In this paper , the authors investigated the performance of three different OAT methods: (1) model-based methods (i.e., classical ray-based OAT using a linearized forward model), (2) data-driven methods (such as deep learning) to directly learn the inverse model, and (3) a hybrid solution, which combines deep learning of the forward model with a standard recursive optimization to estimate SSPs.

TL;DR: This technology and marketdriven trend toward throughputoriented (scale-out) designs implies a major challenge in terms of chip-level power and/or thermal management—in a regime where balanced performance growth (single-thread versus throughput) at affordable power becomes a steeper challenge over time.