Tristan Webb

Title: Innovations of Tristan Webb

Introduction

Tristan Webb is an accomplished inventor based in San Diego, CA. He has made significant contributions to the field of machine learning, holding 2 patents that showcase his innovative approach to optimizing machine learning architectures.

Latest Patents

One of his latest patents is titled "Framework for optimization of machine learning architectures." This patent relates to a framework designed to automatically and efficiently identify machine learning (ML) architectures that are optimized for specific performance metrics and hardware platforms. The framework significantly reduces the time required to find suitable ML architectures compared to manual methods and existing ML model searching techniques. Additionally, it includes a user interface that allows users to search for various ML architectures based on modified search parameters, such as different hardware platform aspects and performance metrics.

Another notable patent is "Methods and arrangements to quantize a neural network with machine learning." This invention involves logic that can quantize a primary neural network. It generates quantization parameters through a secondary neural network, which is trainable with an objective function. The primary neural network consists of multiple layers that can be trained, each containing multiple tensors. The secondary neural network outputs the quantization parameters to these tensors, enhancing the efficiency of the neural network.

Career Highlights

Tristan Webb is currently employed at Intel Corporation, where he continues to push the boundaries of machine learning technology. His work at Intel has allowed him to explore innovative solutions that address complex challenges in the field.

Collaborations

Some of his notable coworkers include Somdeb Majumdar and Ron Banner, who contribute to the collaborative environment that fosters innovation at Intel.

Conclusion

Tristan Webb's contributions to machine learning through his patents reflect his dedication to advancing technology in this rapidly evolving field. His work not only enhances the efficiency of machine learning architectures but also paves the way for future innovations.