Washington, DC, United States of America

Michael Cody Glapa

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):

Title: Innovations of Michael Cody Glapa

Introduction

Michael Cody Glapa is an accomplished inventor based in Washington, DC. He has made significant contributions to the field of artificial intelligence and deep learning. His innovative work focuses on optimizing instructions for deep learning accelerators through advanced compiler technology.

Latest Patents

Michael Cody Glapa holds a patent for a "Compiler with an artificial neural network to optimize instructions generated for execution on a deep learning accelerator of artificial neural networks." This patent describes systems, devices, and methods related to a Deep Learning Accelerator and memory. The integrated circuit device is designed to execute instructions with matrix operands and is configured with random access memory (RAM). The compiler utilizes an artificial neural network to identify optimized compilation options for artificial neural networks and the hardware platforms of Deep Learning Accelerators. The artificial neural network can be trained via machine learning to enhance its ability to identify the best compilation options based on the features of the neural network and the hardware platform.

Career Highlights

Michael is currently employed at Micron Technology Incorporated, where he continues to push the boundaries of technology in the field of deep learning. His work has garnered attention for its innovative approach to optimizing computational processes.

Collaborations

Michael collaborates with Aliasger Tayeb Zaidy, contributing to advancements in their shared field of expertise.

Conclusion

Michael Cody Glapa's contributions to the field of artificial intelligence and deep learning through his innovative patent demonstrate his commitment to advancing technology. His work at Micron Technology Incorporated continues to influence the development of efficient computational methods.

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