Tempe, AZ, United States of America

Elham Azari

This inventor holds 1 USPTO granted patent. Top assignee: Arizona State University. Active years: 2023.


% Patents Active = 100.0

Average Co-Inventor Count = 2.0

ph-index = 1


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Elham Azari: Innovator in Neural Network Circuitry

Introduction

Elham Azari is a prominent inventor based in Tempe, AZ (US). He has made significant contributions to the field of neural network circuitry, particularly through his innovative patent.

Latest Patents

Elham Azari holds a patent for "Neural network circuitry having approximate multiplier units." This invention discloses neural network circuitry that includes a first plurality of logic cells interconnected to form neural network computation units. These units are designed to perform approximate computations. The circuitry also features a second plurality of logic cells that create a controller hierarchy, interfacing with the computation units to manage the pipelining of approximate computations. Notably, the computation units incorporate approximate multipliers that execute approximate multiplications, which are essential for the computations. The approximate multipliers are equipped with preprocessing units that effectively reduce latency while maintaining accuracy.

Career Highlights

Elham Azari is affiliated with Arizona State University, where he continues to advance research in neural networks and related technologies. His work has garnered attention for its potential applications in various fields, including artificial intelligence and machine learning.

Collaborations

Elham collaborates with Sarma Vrudhula, who is also involved in research related to neural networks. Their partnership enhances the depth and breadth of their innovative projects.

Conclusion

Elham Azari's contributions to neural network circuitry exemplify the intersection of technology and innovation. His patent reflects a commitment to advancing computational efficiency and accuracy in neural networks.

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