Industry Rising Star 2024: Dwith Chenna
Dwith Chenna, Advanced Micro Devices (AMD), San Jose
Nominated by: Dr. Sai Deepika Regani
Dwith Chenna, is a distinguished research and development professional with a specialization in algorithm development and optimization in computer vision, deep learning, and Edge AI. With over 10 years of international experience, he excels in creating state-of-the-art, performance-critical perception systems and has a deep understanding of the complexities involved in developing and optimizing deep learning models on resource-constrained hardware like Digital Signal Processors (DSP). His primary focus is on addressing the challenges of performance, accuracy and driving key performance metrics such as latency, memory, bandwidth, and power consumption—often through the integration and development of tooling and automation. Dwith’s expertise extends to quantizing, optimizing, and tuning the performance of deep learning models, making significant strides in enhancing efficiency and accuracy.
Dwith’s impactful roles at prestigious organizations like Magic Leap, Center of Devices and Radiology Health (CDRH) at FDA, and currently at AMD underscore his broad influence and technical excellence. His work in augmented reality glasses at Magic Leap and non-contact fever screening at the FDA showcases his innovative approach and practical solutions to real-world challenges. He is recognized not only for his technical contributions but also for his leadership and dedication to advancing the field. As a technical committee member, reviewer for prestigious conferences, judge for AI excellence awards, and speaker at technology summits, Dwith actively fosters growth and innovation in technology.
Industry Rising Star Award is a testament to his outstanding contributions and the potential for future achievements that will continue to shape the global technology landscape. Congratulations to Dwith Chenna for this well-deserved honor, celebrating his remarkable impact and unwavering commitment to excellence in the tech community.
Awards and Honors:
- IEEE (Institute of Electrical and Electronics Engineers) Senior Member
- Consumer Advisor board member: AI Accelerator Institute (AIAI)
- Computer Vision and AI Inference, Advisor at Tola Capital
- Member of IEEE Senior Membership evaluation review panel
- Editor of Computer Science FeedForward Magazine of IEEE Region 6, SCV
- IEEE Spectrum editorial advisory board member
- Advisor/Panelist: 3rd Annual IEEE SA Telehealth Pitch Competition
- ORISE Research program, Center of Devices and Radiology Health (CDRH) at FDA
- Invited speaker at Embedded Vision Summit 2024 (https://embeddedvisionsummit.com/2024/session/dnn-quantization-theory-to-practice/ )
- Invited speaker for Technical Sights session at Embedded Vision Summit 2023. (https://embeddedvisionsummit.com/2023/session/practical-approaches-to-dnn-quantization/ )
- Speaker at the ACM technical speaker series in Bay area (https://www.bayareascience.org/calendar/index.php?eID=36288 )
Publications:
- EdgeAI in Self-Sustaining Systems With AI and IoT [Link]
- Advancements in Healthcare: Harnessing Machine Learning for Medical Devices [Link]
- From Theory to Practice: Quantizing Convolutional Neural Networks for Practical Deployment [Link]
- Evolution of Convolutional Neural Network (CNN): Compute vs Memory bandwidth for Edge AI [Link]
- Quantization of Convolutional Neural Networks: A Practical Approach [Link]
- Free-form deformation approach for registration of visible and infrared facial images in fever screening [Link]
- Multi-modality image registration for effective thermographic fever screening [Link]
- Facial and oral temperature data from a large set of human subject volunteers [Link]
- Parallel implementation of LBP based face recognition on GPU using OpenCL [Link]
For more, please visit the Google scholar page: Dwith Chenna