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Full-Text Articles in Physical Sciences and Mathematics
Deepsz: A Novel Framework To Compress Deep Neural Networks By Using Error-Bounded Lossy Compression, Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello
Deepsz: A Novel Framework To Compress Deep Neural Networks By Using Error-Bounded Lossy Compression, Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello
Computer Science Faculty Research & Creative Works
Today's deep neural networks (DNNs) are becoming deeper and wider because of increasing demand on the analysis quality and more and more complex applications to resolve. The wide and deep DNNs, however, require large amounts of resources (such as memory, storage, and I/O), significantly restricting their utilization on resource-constrained platforms. Although some DNN simplification methods (such as weight quantization) have been proposed to address this issue, they suffer from either low compression ratios or high compression errors, which may introduce an expensive fine-tuning overhead (i.e., a costly retraining process for the target inference accuracy). In this paper, we propose DeepSZ: …
An Improved Algorithm For Generating Minimal Perfect Hash Functions, Thomas J. Sager
An Improved Algorithm For Generating Minimal Perfect Hash Functions, Thomas J. Sager
Computer Science Technical Reports
A minimal perfect hash function (MPHF) is a function from a set of M objects to the first M non-negative integers. MPHF's are useful for the compact storage and fast retrieval of frequently used objects such as reserved words in a programming language or commonly employed words in a natural language. In this paper we improve on an earlier result and present an algorithm for generating MPHF's with an expected time complexity proportional to M4. We also give a MPHF for the 256 most frequently used words in the English language.