K
Ken Mai
Researcher at Carnegie Mellon University
Publications - 79
Citations - 6276
Ken Mai is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Flash memory & CMOS. The author has an hindex of 30, co-authored 76 publications receiving 5858 citations. Previous affiliations of Ken Mai include Stanford University.
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Journal ArticleDOI
The future of wires
R. Ho,Ken Mai,Mark Horowitz +2 more
TL;DR: Wires that shorten in length as technologies scale have delays that either track gate delays or grow slowly relative to gate delays, which is good news since these "local" wires dominate chip wiring.
Proceedings ArticleDOI
Smart Memories: a modular reconfigurable architecture
TL;DR: Simulations of the mappings show that the Smart Memories architecture can successfully map two very different machines at opposite ends of the architectural spectrum, the Imagine stream processor and the Hydra speculative multiprocessor, with only modest performance degradation.
Proceedings ArticleDOI
Error patterns in MLC NAND flash memory: measurement, characterization, and analysis
TL;DR: A framework for fast and accurate characterization of flash memory throughout its lifetime is designed and implemented and distinct error patterns, such as cycle-dependency, location- dependency and value- dependency, for various types of flash operations are demonstrated.
Proceedings ArticleDOI
Multi-bit Error Tolerant Caches Using Two-Dimensional Error Coding
TL;DR: Two-dimensional (2D) error coding in embedded memories is proposed, a scalable multi-bit error protection technique to improve memory reliability and yield and it is shown that 2D error coding can correct clustered errors up to 32times32 bits with significantly smaller performance, area, and power overheads than conventional techniques.
Proceedings ArticleDOI
Threshold voltage distribution in MLC NAND flash memory: characterization, analysis, and modeling
TL;DR: A key result is that the threshold voltage distribution can be modeled, with more than 95% accuracy, as a Gaussian distribution with additive white noise, which shifts to the right and widens as P/E cycles increase.