@article{oai:nagoya.repo.nii.ac.jp:02002806,
 author = {Hou, Lingxiao and Masuda, Yutaka and Ishihara, Tohru},
 journal = {ASP-DAC 2022 : 27th Asia and South Pacific Design Automation Conference : Proceedings},
 month = {Feb},
 note = {The logarithmic approximate multiplier proposed by Mitchell provides an efficient alternative to accurate multipliers in terms of area and power consumption. However, its maximum error of 11.1% makes it difficult to deploy in applications requiring high accuracy. To widely reduce the error of the Mitchell multiplier, this paper proposes a novel operand decomposition method which decomposes one operand into multiple operands and calculates them using multiple Mitchell multipliers. Based on this operand decomposition, this paper also proposes an accuracy reconfigurable vector accelerator which can provide a required computational accuracy with a high parallelism. The proposed vector accelerator dramatically reduces the area by more than half from the accurate multiplier array while satisfying the required accuracy for various applications. The experimental results show that our proposed vector accelerator behaves well in image processing and robot localization., 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC). 17-20 Jan. 2022. Taipei, Taiwan},
 pages = {568--573},
 title = {An Accuracy Reconfigurable Vector Accelerator Based on Approximate Logarithmic Multipliers},
 year = {2022}
}