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Manish Amde

Researcher at University of California, San Diego

Publications -  13
Citations -  1768

Manish Amde is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Spread spectrum & Network packet. The author has an hindex of 6, co-authored 13 publications receiving 1620 citations. Previous affiliations of Manish Amde include University of California & Indian Institute of Technology Bombay.

Papers
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Journal Article

MLlib: machine learning in apache spark

TL;DR: MLlib as mentioned in this paper is an open-source distributed machine learning library for Apache Spark that provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives.
Posted Content

MLlib: Machine Learning in Apache Spark

TL;DR: MLlib as discussed by the authors is an open-source distributed machine learning library for Apache Spark that provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives.
Journal ArticleDOI

Asynchronous on-chip networks

TL;DR: The authors survey various methodologies used for leveraging asynchronous on-chip communication and investigate various GALS based implementations, desynchronisation strategies and asynchronous network-on-chip (NoC) designs.
Proceedings ArticleDOI

Automating the design of an asynchronous DLX microprocessor

TL;DR: The automated design of an asynchronous DLX microprocessor has been designed beginning with a standard RTL-like Verilog specification and the Pipefitter design flow has been used to automatically generate both the specification for the direct implementation of the Control Unit and a synthesisable Verilogs specification of the Data Path.
Patent

Churn prediction in a broadband network

TL;DR: In this article, a churn predictor is built and trained from data collected from multiple customers, including static configuration data and dynamic measured data, and the churn predictor processes the customer instance and generates a churn likelihood score.