A framework for designing power-efficient inference accelerators in tree-based learning applications

2022 ◽  
Vol 109 ◽  
pp. 104638
Author(s):  
Brunno Abreu ◽  
Mateus Grellert ◽  
Sergio Bampi
Keyword(s):  
2017 ◽  
Vol 5 (4) ◽  
pp. 15
Author(s):  
ISWARIYA S. ◽  
RAJA M. VILASINI ◽  
◽  
Keyword(s):  

2003 ◽  
Vol 49 (4) ◽  
pp. 1067-1072 ◽  
Author(s):  
Yang-Ick Joo ◽  
Tae-Jin Lee ◽  
Doo Seop Eom ◽  
Yeonwoo Lee ◽  
Kyun Hyon Tchah

Nanophotonics ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 937-945
Author(s):  
Ruihuan Zhang ◽  
Yu He ◽  
Yong Zhang ◽  
Shaohua An ◽  
Qingming Zhu ◽  
...  

AbstractUltracompact and low-power-consumption optical switches are desired for high-performance telecommunication networks and data centers. Here, we demonstrate an on-chip power-efficient 2 × 2 thermo-optic switch unit by using a suspended photonic crystal nanobeam structure. A submilliwatt switching power of 0.15 mW is obtained with a tuning efficiency of 7.71 nm/mW in a compact footprint of 60 μm × 16 μm. The bandwidth of the switch is properly designed for a four-level pulse amplitude modulation signal with a 124 Gb/s raw data rate. To the best of our knowledge, the proposed switch is the most power-efficient resonator-based thermo-optic switch unit with the highest tuning efficiency and data ever reported.


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