From Integrated Circuits to AI at the Edge: Fundamentals of Deep Learning & Data-Driven Hardware
About This Video
In this workshop, I would like to share my journey transitioning from an electrical engineer focusing on ultra-low power integrated circuit design to an AI Solution Architect. Through specific examples of how the two fields connect, I will discuss the fundamentals of deep learning and data-driven hardware design. I will start with my experience in the semiconductor industry designing application-specific and data-dependent hardware for IoT systems and then discuss how this experience led to my career in AI specializing in areas including high-performance computing, edge computing, and more recently, federated learning.
I hope the attendees will not only find the technical content informative but also see how a growth mindset truly helped me find my career passion. Having a broad knowledge of the eco-system that supports AI applications – such as the hardware stack, hardware level optimization, and application-specific hardware design – can be very helpful to understanding and choosing the right platform for operational AI. I also hope to use this opportunity to connect with fellow AI/hardware enthusiasts in WiDS.
This workshop was conducted by Chu Lahlou, AI Specialized Cloud Solution Architect at Microsoft.
In This Video
AI Specialized Cloud Solution Architect, Microsoft
As a member of the Microsoft WW AI Specialist Team, Chu is responsible for providing hands-on, AI technical intensity to help progress customers’ AI journey.
Chu has experience architecting artificial intelligence and machine learning solutions up and down the stack spanning hardware, edge, cloud-based platforms for computer vision, natural language, ethical AI and more. She’s led large agile organizations in developing and implementing proof-of-concepts to enterprise-level, hardware-accelerated AI solutions for Federal Civil, Civilian, and Health agencies. Prior to joining Microsoft, Chu led analog integrated circuit design and machine learning work at MIT, NVIDIA, and Texas Instruments, and AI/HPC business and capability development at Booz Allen Hamilton.
Chu received an M.S. in Electrical Engineering and Computer Science from MIT. She has published in and served as a reviewer for IEEE conferences and is currently an MIT Educational Counselor supporting recruitment in the Washington metropolitan area.