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Extending sparse tensor accelerators to support multiple compression formats

Proceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021

Qin, Eric; Jeong, Geonhwa; Won, William; Kao, Sheng C.; Kwon, Hyoukjun; Das, Dipankar; Moon, Gordon E.; Rajamanickam, Sivasankaran R.; Krishna, Tushar

Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential memory and compute savings. These applications use data stored in a variety of compression formats. We demonstrate that both the compactness of different compression formats and the compute efficiency of the algorithms enabled by them vary across tensor dimensions and amount of sparsity. Since DL and scientific workloads span across all sparsity regions, there can be numerous format combinations for optimizing memory and compute efficiency. Unfortunately, many proposed accelerators operate on one or two fixed format combinations. This work proposes hardware extensions to accelerators for supporting numerous format combinations seamlessly and demonstrates ∼ 4 × speedup over performing format conversions in software.

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TYPE Conference Paper YEAR 2021
DOIOSTIScopus

Data-driven learning of nonautonomous systems

SIAM Journal on Scientific Computing

Qin, Tong; Chen, Zhen; Jakeman, John D.; Xiu, Dongbin

We present a numerical framework for recovering unknown nonautonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the nonautonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.

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TYPE Journal Article YEAR 2021
DOIOSTIScopus
Results 626–650 of 9,998
Results 626–650 of 9,998