Location History:
- Malappuram, IN (2020)
- Hyderabad, IN (2023)
Company Filing History:
Years Active: 2020-2023
Title: Abid Karumannil: Innovator in Machine Learning and Neural Network Technologies
Introduction
Abid Karumannil is a prominent inventor based in Malappuram, India. He has made significant contributions to the fields of machine learning and neural network technologies. With a total of 2 patents, his work focuses on optimizing the processing of machine learning tasks and enhancing the architecture of neural network accelerators.
Latest Patents
Abid's latest patents include innovative methods for scheduling the processing of machine learning tasks on heterogeneous compute circuits. This patent describes a system where a computer processor instantiates kernel objects in response to kernel definitions. Each kernel object corresponds to a specific compute circuit, allowing for efficient task management through a generated graph in memory. The tasks are organized into queues, enabling threads to execute them based on their assignments.
Another notable patent is for a compiler and hardware-abstraction-layer architecture designed for a programmable integrated circuit (IC). This method involves mapping and porting a neural network to an IC by generating a framework-independent network graph and configuring the IC based on an execution sequence vector derived from the network description.
Career Highlights
Abid Karumannil is currently employed at Xilinx, Inc., where he applies his expertise in machine learning and neural networks. His work at Xilinx has positioned him as a key player in advancing technology in these areas.
Collaborations
Abid collaborates with talented professionals such as Vishal K Jain and Kumar S S Vemuri, contributing to innovative projects and research in the field.
Conclusion
Abid Karumannil's contributions to machine learning and neural network technologies demonstrate his commitment to innovation and excellence. His patents reflect a deep understanding of complex systems and a drive to enhance computational efficiency.