For Dwith Chenna, actively engaging with professional organizations isn’t an obligation, it’s enlightened self-interest.
Through this work with IEEE Computer Society and other organizations, he regularly connects with and learns from other professionals that he’d never encounter in daily life; through his work with conferences and publications, he engages with the latest research in his field, which feeds his own cutting-edge interests in creating solutions in computer vision, deep learning, and Edge AI.
Chenna, an MTS product development engineer in AI inference at AMD, was named Industry Rising Star 2024 by the IEEE Computer Society Santa Clara Valley Chapter and one of Computing’s Top 30 Early Career Professionals for 2024.
In the Q&A that follows, Chenna discusses his passion for his research and development, and how his contributions to the profession feed this work and his drive. Specifically, he describes
How being named an IEEE Rising Star had three key impacts on his career
The practical benefits of his work using fix-point arithmetic to implement CNNs on digital signal processors
The ways in which his various IEEE affiliations have enriched both his professional and personal lives
The three-pronged strategy he uses in his editorial work to ensure quality, technical accuracy, and an environment that encourages innovation and dialogue
The key themes he emphasizes in his invited talks, which aim for clear, actionable, research-driven messages
You were named the Industry Rising Star by the IEEE Computer Society Santa Clara Valley Chapter in 2024. What do you believe contributed to this recognition, and how has it impacted your career?
I am deeply honored to have been recognized as the Industry Rising Star by the IEEE Computer Society Santa Clara Valley Chapter in 2024. Several factors contributed to this recognition:
Innovative projects and technical expertise. Over the past several years, I have dedicated myself to developing state-of-the-art solutions in computer vision, embedded algorithms, and efficient AI/ML deployment. At AMD, I use my technical expertise to develop comprehensive onboarding resources, such as use cases, tutorials, and methodology documents, which facilitate the seamless adoption of AI solutions across various platforms. This holistic approach ensures successful implementation and fosters innovation within the industry. Additionally, my work on the fixed-point implementation of convolutional neural networks on digital signal processors has helped in developing tools that enhance the performance and accuracy of AI inference on resource-constrained hardware. Building strong, cross-functional teams has been essential to my success. By fostering an environment of collaboration and open communication, I’ve been able to harness diverse perspectives, leading to more robust and innovative solutions. Additionally, mentoring junior team members has not only contributed to their growth but also reinforced a culture of excellence within the organization.
Leading initiatives like these has allowed me to push the boundaries of what is possible in Edge AI and computer vision. My efforts have resulted in publications, including white papers like Edge AI: Quantization as the Key to On-Device Smartness and Evolution of Convolutional Neural Network (CNN) Compute vs Memory Bandwidth for Edge AI.
These works feature technical insights that are useful to the engineers and developers working in the field and to the technical community as a whole. Moreover, my commitment to optimizing AI/ML deployment is evident in projects. While working at Magic Leap on cutting-edge augmented reality glasses, the great work done by our team was recognized, including by the Edge AI Innovation Award at the Embedded Vision Summit 2024.
Commitment to continuous learning. Staying abreast of the latest technological advancements has been pivotal. I actively engage in professional development through speaking sessions in conferences/summits and contributing to open-source communities. This dedication has enabled me to integrate novel technologies and best practices into my work, enhancing both quality and efficiency. Participating in IEEE conferences, publishing papers, and speaking at industry events have allowed me to share insights and contribute to the broader tech community. These activities have not only elevated my professional profile but also facilitated valuable connections and collaborations.
My focus on cutting-edge projects, combined with a strong foundation in research and development, has been instrumental in driving my technical advancement in efficient AI and computer vision, ultimately contributing to my recognition as an Industry Rising Star.
The award’s impact. Receiving this recognition has had a profound impact on my career in several ways:
Enhanced visibility. Being acknowledged by a prestigious organization like IEEE has increased my visibility within the industry, opening doors to new opportunities and collaborations with leading experts. The accolade has bolstered my confidence and motivation to take on more challenging projects, pursue leadership roles, and continue pushing the envelope in my field.
Leadership. With recognition comes trust. I’ve been entrusted with more significant leadership responsibilities, allowing me to make a more substantial impact within my organization and the industry at large.
Networking opportunities. Connections made through IEEE and related events have been invaluable, providing a support network of peers and mentors who inspire and guide me in my professional journey.
Overall, being named the Industry Rising Star has been a pivotal milestone in my career, validating my efforts and inspiring me to continue striving for excellence and innovation in the tech industry.
As a Senior Member of IEEE since 2023 and Technical Program Committee for IEEE conferences, how do you stay engaged with the IEEE community, and what value do you find in these professional affiliations?
As a Senior Member of IEEE since 2023 and an active member of various Technical Program Committees for IEEE conferences, I maintain a deep and ongoing engagement with the IEEE community through multiple avenues. My commitment to IEEE involves active participation in technical committees, peer-reviewing, advisory roles, and contributing to the organization’s initiatives. This engagement not only allows me to contribute meaningfully to the field of computer vision and Edge AI but also fosters my professional growth and keeps me at the forefront of technological advancements.
Serving on the Technical Program Committees for prestigious IEEE conferences such as the International Conference on Computer and Applications and the IEEE Conference on Artificial Intelligence has been instrumental in my engagement with the IEEE community. In these roles, as reviewer, I evaluate cutting-edge research submissions, and highlight innovative developments in computer vision, deep learning, and Edge AI. This involvement ensures that I am consistently exposed to the latest research trends and methodologies, allowing me to integrate new insights into my own work at AMD. In addition, as a dedicated peer reviewer for numerous IEEE publications and conferences, including the IEEE International Workshop on Machine Learning for Signal Processing and SPIE AR/VR/MR conferences, I contribute to maintaining the high standards of scholarly communication within the community. Reviewing papers not only allows me to provide constructive feedback to fellow researchers but also keeps me abreast of emerging technologies and novel approaches in my areas of expertise. This continuous interaction with a broad spectrum of research enhances my ability to identify and implement best practices in my projects.
Advisory roles and bridging academia and industry. As an IEEE Spectrum Advisory Board Member, I play a crucial role in shaping the content and strategic direction of one of IEEE’s most influential publications. My responsibilities include participating in editorial planning sessions, contributing to the development of special issues, and providing insights on the latest research trends. By attending and leading workshops and technical discussions, I help advance collective knowledge and address critical challenges within these fields. This role not only allows me to influence the dissemination of cutting-edge information but also fosters a collaborative environment where industry leaders and researchers can exchange ideas and drive innovation forward.
In addition to my advisory role with IEEE Spectrum, I serve as an advisor and judge for the Annual IEEE Standards Association Telehealth Pitch Competition. This position involves evaluating innovative telehealth solutions that enhance healthcare delivery and patient outcomes. As a judge, I assess submissions for their technical excellence, practical feasibility, and potential impact on the telehealth industry. My feedback helps participants refine their projects, ensuring that the most promising ideas receive the support and recognition they deserve. This involvement not only promotes the adoption of advanced AI solutions in telehealth but also fosters meaningful collaborations between academia and industry, driving technological advancements that address pressing healthcare needs.
Value derived from IEEE professional affiliations. The value I find in my IEEE affiliations is both professional and personal. IEEE provides unparalleled access to a global network of experts, enabling collaborations that drive innovation and facilitate the exchange of ideas. The continuous learning opportunities available through conferences, webinars, and IEEE publications ensure that I remain well informed about the latest technological advancements and research breakthroughs. This knowledge is essential for maintaining a competitive edge in my work at AMD, where I develop and optimize AI solutions for deployment on advanced hardware platforms like AI accelerators.
Moreover, being part of the IEEE community instills a sense of belonging and shared purpose. Engaging with peers through forums, working groups, and collaborative projects not only inspires creative problem-solving but also encourages the exchange of diverse perspectives. This collaborative environment is instrumental in advancing my research and professional initiatives, enabling me to contribute to and benefit from collective expertise.
Lastly, my involvement with IEEE aligns with my commitment to mentoring the next generation of engineers and researchers. By participating in IEEE’s educational initiatives and providing guidance through my advisory roles, I help cultivate talent and inspire others to pursue excellence in the fields of computer vision and AI.
Your multiple roles as a Consumer Advisor Board member and AI ambassador at the AI Accelerator Institute involve providing insights into AI advancements. Can you share a recent project or initiative you advised on and its impact on the industry?
In my roles at the AI Accelerator Institute, I recently had the opportunity to advise the community on optimizing AI inference performance for hardware accelerators and expanding the practical use of Edge AI. This effort culminated in two technical blog articles that I contributed:
“Improving AI Inference Performance with Hardware Accelerators.” In this piece, I detailed how leveraging specialized hardware can drastically reduce latency and boost throughput for AI models. I detailed and explored best practices in algorithm optimization, showcasing how tailored hardware solutions deliver efficient computation without compromising on accuracy. This article has not only enhanced understanding of performance bottlenecks but also provided engineers and developers with actionable insights to integrate these advancements in their own projects.
“AI Inference in Edge Computing: Benefits and Use Cases.” Here, I delved into the practical benefits Edge AI brings to various application domains. The blog highlighted successful implementations, discussed deployment challenges, and outlined future directions for enabling intelligent processing directly on edge devices. The blog has been useful in providing insights into bridging the gap between theoretical research and industry application, fostering a broader adoption of robust edge computing solutions.
Both articles have initiated ongoing technical discussions in the community, enabling insights that are crucial for projects between academia and industry. By providing a detailed technical understanding of how hardware accelerators and Edge AI can be harnessed to achieve superior performance, these contributions play an important role in driving innovation and practical advancements within the field of AI.
Your editorial roles at IEEE SCV’s FeedForward magazine and