PISCES: Power-Aware Implementation of SLAM by Customizing Efficient Sparse Algebra
Published in 2020 57th ACM/IEEE Design Automation Conference (DAC), 2020
Citation: Bahar Asgari, Ramyad Hadidi, Nima Shoghi, Hyesoon Kim, 2020 57th ACM/IEEE Design Automation Conference (DAC), 2020. https://doi.org/10.1109/DAC18072.2020.9218550
Introduces Pisces, a method that optimizes power consumption and latency for simultaneous localization and mapping (SLAM). Through using sparse data and reducing memory access, it results in a 2.5 times power reduction and 7.4 times faster execution than other contemporary methods.
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