Engineering the Next Generation of Smart Systems: Q&A with Manvinder “Manny” Singh

Edge AI is revolutionizing the way engineers develop smart devices, opening up new possibilities for on-device intelligence. Manvinder “Manny” Singh, VP of Product Management at Qualcomm Technologies, discusses how the company is simplifying machine learning development and empowering embedded engineers through its partnership with Edge Impulse. Qualcomm Technologies has been embedding AI capabilities into its Snapdragon® mobile platforms for nearly a decade, laying the foundation for edge AI with components like the Qualcomm® Hexagon™ DSP, Adreno™ GPU, and Kryo™ CPU.

Qualcomm Technologies recognized the need for a unified AI development experience across its diverse portfolio of SoCs, leading to the discovery of Edge Impulse over a year ago. Edge Impulse provides a developer-friendly platform that streamlines data collection, model training, and deployment on Qualcomm Technologies-powered devices. This partnership aligns with Qualcomm’s long-term vision of enabling intelligent, autonomous systems across various domains, accelerating the democratization of edge AI.

With the acquisition of Edge Impulse, Qualcomm Technologies has enhanced the end-to-end ML workflow for developers, offering a seamless interface for data ingestion, labeling, model training, and deployment. This streamlined process enables developers to go from concept to deployment in days, leveraging pre-integrated dev kits and GPU training. The shift towards a platform-first strategy reinforces Qualcomm Technologies’ commitment to supporting developers and ecosystem partners.

In the industrial IoT sector, AI is poised to become the nervous system, enabling predictive maintenance, adaptive control, and real-time decision-making. The evolution of AI at the edge presents technical challenges such as balancing compute performance with power efficiency, ensuring model robustness in noisy environments, and enabling secure, low-latency inference. Qualcomm Technologies is addressing these challenges through dedicated AI accelerators and optimized software toolchains.

As edge AI continues to advance, fully autonomous IoT systems powered by on-device intelligence are becoming a reality. This transformation is reshaping industrial and consumer applications, enabling efficiency, safety, and personalization without relying on cloud connectivity. The future of AI at the edge holds the promise of predictive capabilities that can anticipate problems or opportunities before humans even recognize them, paving the way for new breakthroughs in environmental sensing, personalized healthcare, and autonomous robotics.