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)|
|Title of host publication||GLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI|
|Publisher||Association for Computing Machinery|
|Number of pages||6|
|State||Published - May 30 2018|
|Event||28th Great Lakes Symposium on VLSI, GLSVLSI 2018 - Chicago, United States|
Duration: May 23 2018 → May 25 2018
|Name||Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI|
|Other||28th Great Lakes Symposium on VLSI, GLSVLSI 2018|
|Period||5/23/18 → 5/25/18|
Bibliographical notePublisher Copyright:
© 2018 Association for Computing Machinery.