Hillsboro, OR, United States of America

Derssie Mebratu

This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Intel Corporation. Active years: 2023.

USPTO Granted Patents = 1 

% Patents Active = 100.0

Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Innovations of Derssie Mebratu in Computer Hardware Performance Prediction.

Introduction

Derssie Mebratu is an accomplished inventor based in Hillsboro, OR (US). He has made significant contributions to the field of computer hardware performance prediction through innovative technologies. His work focuses on utilizing machine learning to enhance the reliability and efficiency of computing devices.

Latest Patents

Derssie Mebratu holds 1 patent for his invention titled "Technologies for predicting computer hardware performance with machine learning." This patent discloses methods for analyzing telemetry data using machine learning and statistical modeling. The technology aims to identify potential failures in various components of a compute device, such as fans or memory, which could impact overall performance. For instance, machine-learning algorithms can assess how memory access latency affects the execution time of workloads.

Career Highlights

Derssie Mebratu is currently employed at Intel Corporation, a leading technology company known for its innovations in semiconductor manufacturing and computing solutions. His role at Intel allows him to work on cutting-edge technologies that shape the future of computing.

Collaborations

Some of his notable coworkers include Samantha Alt and Nishi Ahuja, who collaborate with him on various projects within the company.

Conclusion

Derssie Mebratu's contributions to the field of computer hardware performance prediction exemplify the impact of innovative technologies in enhancing device reliability. His work at Intel Corporation continues to influence advancements in machine learning applications for computing.

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