Raanana, Israel

Oded Gabbay


Average Co-Inventor Count = 2.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Oded Gabbay: Innovator in Machine Learning Technology

Introduction

Oded Gabbay is a prominent inventor based in Raanana, Israel. He has made significant contributions to the field of machine learning, particularly in the area of low latency execution of machine learning models. His innovative approach has the potential to enhance the efficiency of machine learning processing.

Latest Patents

Oded Gabbay holds a patent for a groundbreaking apparatus designed for machine learning processing. The patent, titled "Low latency execution of a machine learning model," describes a system that includes computational engines and a Central Processing Unit (CPU). The CPU is configured to receive a work plan for processing samples according to a machine learning model represented by a corresponding ML graph. This work plan specifies the jobs required for executing at least a subgraph of the ML graph, which can operate independently when the inputs are valid. The CPU also pre-processes a partial subset of jobs in the work plan to produce a group of pre-processed jobs, which are then submitted to the computational engines for execution. Oded Gabbay has 1 patent to his name.

Career Highlights

Oded Gabbay is currently employed at Habana Labs Ltd., a company known for its advancements in artificial intelligence and machine learning technologies. His work at Habana Labs has positioned him as a key player in the development of innovative solutions that leverage machine learning for improved performance.

Collaborations

Oded collaborates with Oren Kaidar, contributing to the advancement of machine learning technologies through their combined expertise.

Conclusion

Oded Gabbay's contributions to machine learning, particularly through his patented technology, highlight his role as an innovator in the field. His work continues to influence the development of efficient machine learning processing systems.

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