Maximal Entropy Reduction Algorithm for SAR ADC Clock Compression
November 07, 2018 Β· Declared Dead Β· π IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Arkady Molev-Shteiman, Xiao-Feng Qi
arXiv ID
1811.11102
Category
eess.SP: Signal Processing
Cross-listed
cs.NI
Citations
0
Venue
IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems
Last Checked
4 months ago
Abstract
Reduction of comparison cycles leads to power savings of a successive-approximation-register (SAR) analog-to-digital converters (ADC). We establish that the lowest average number of comparison cycles of a SAR ADC approaches the entropy of the ADC output, and proposed a simple adaptive algorithm that approaches this lower bound. Today's SAR ADC uses binary search, which consumes more power than necessary for non-uniform input distributions commonly found in practice. We refer to a SAR ADC employing such algorithm the maximal entropy reduction (MER) ADC.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Signal Processing
R.I.P.
π»
Ghosted
π
π
The Cartographer
1D Convolutional Neural Networks and Applications: A Survey
R.I.P.
π»
Ghosted
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
π
π
The Cartographer
Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond
R.I.P.
π»
Ghosted
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
R.I.P.
π»
Ghosted
A New Wireless Communication Paradigm through Software-controlled Metasurfaces
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted