Rachel Jean Trimble

Title: Innovations by Rachel Jean Trimble

Introduction

Rachel Jean Trimble is an accomplished inventor based in Grindleford, GB. She has made significant contributions to the field of machine learning, holding a total of three patents. Her work focuses on enhancing data processing techniques, particularly in the realm of volatile storage and convolution operations.

Latest Patents

One of her latest patents is titled "Selective bit inversion in storage operations for machine learning." This invention involves a processing unit that includes volatile storage for machine learning data in binary representation. The processing unit is designed to selectively invert bit values in binary representations of portions of the machine learning data during storage operations. Additionally, a computer-implemented method is provided, which includes receiving a request to perform a storage operation and executing the method to prioritize one of two potential bit values.

Another notable patent is "Convolution size prediction to reduce calculations." This invention features a data processing apparatus that performs machine learning by convolving neighboring regions of input data using a kernel. The apparatus includes circuitry for max-pooling, which selects the largest convolution outputs as pooled outputs. Prediction circuitry performs size predictions of the convolution outputs based on neighboring regions, adjusting the behavior of the convolution circuitry accordingly.

Career Highlights

Rachel is currently employed at Arm Limited, where she continues to innovate in the field of machine learning. Her work has been instrumental in advancing data processing technologies, making significant impacts in various applications.

Collaborations

Throughout her career, Rachel has collaborated with notable colleagues, including Daren Croxford and Sharjeel Saeed. These collaborations have contributed to her success and the development of her innovative patents.

Conclusion

Rachel Jean Trimble is a pioneering inventor whose work in machine learning has led to significant advancements in data processing techniques. Her contributions are shaping the future of technology and enhancing the capabilities of machine learning systems.