Abstract
This paper presents a novel incremental-precision classification approach that leads to a reduction in energy consumption of linear classifiers for IoT applications. Features are first input to a low-precision classifier. If the classifier successfully classifies the sample, then the process terminates. Otherwise, the classification performance is incrementally improved by using a classifier of higher precision. This process is repeated until the classification is complete. The argument is that many samples can be classified using the low-precision classifier, leading to a reduction in energy. To achieve incremental-precision, a novel data-path decomposition is proposed to design of fixed-width adders and multipliers. These components improve the precision without recalculating the outputs, thus reducing energy. Using a linear classification example, it is shown that the proposed incremental-precision based multi-level classifier approach can reduce energy by about 41% while achieving comparable accuracies as that of a full-precision system.
Original language | English (US) |
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Title of host publication | GLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI |
Publisher | Association for Computing Machinery |
Pages | 291-296 |
Number of pages | 6 |
ISBN (Electronic) | 9781450357241 |
DOIs | |
State | Published - May 30 2018 |
Event | 28th Great Lakes Symposium on VLSI, GLSVLSI 2018 - Chicago, United States Duration: May 23 2018 → May 25 2018 |
Publication series
Name | Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI |
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Other
Other | 28th Great Lakes Symposium on VLSI, GLSVLSI 2018 |
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Country/Territory | United States |
City | Chicago |
Period | 5/23/18 → 5/25/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computing Machinery.