PBL
AI in Hardware
Design and implement AI-driven hardware solutions for wearables, autonomous systems, and next-gen semiconductor optimization.
Project
Location
Online
Duration
8 Weeks
Upcoming Sessions
Mar. 09 - May 03, 2026
Outcomes
Perform system integration of AI with real-world hardware platforms
Find AI model deployment on specialized hardware (TPUs, GPUs)
Repurpose Computer vision for self-driving and robotics applications
Assess autonomous navigation and sensor fusion techniques
Implement reinforcement learning for AI-powered semiconductor design
You Will Get
Industry Guidance
Work directly with our project leads—experts and top researchers—who bring their real-world insights and expertise straight to your learning experience.
Research Experience
Collaborate with teammates and the project lead in a multi-week project to pursue novel questions in your research field.
Peer Networks
Engage with our PBL participants from all over the world. Collaborate with new peers and learn about their own research endeavours.
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A Strong Portfolio
Put your best foot forward in the PBL with a standout project and receive a PBL Evaluation Report that can be used as a recommendation letter for employers and grad schools.
Expert Guidance
Get personalized feedback to grow your research and innovation skills.
Deliverables
Real projects, lasting connections, and new opportunities beyond your program.
Project Deliverables
The final presentation of your 8 weeks could be a poster, written report, or a slide deck, all of which can be expanded on.
Research Extension
Utilize up to 5 additional meeting times with the project lead after the project’s conclusion to build your work out for publication or conference presentation.
Industry Network
Meet peers in your projects and participate in a global talent community both online and in-person.
Industry Application
Apple integrates AI-driven hardware and edge computing across its product ecosystem, from Apple Silicon chips to AI-powered wearables like the Apple Watch and Vision Pro. This PBL directly aligns with Apple’s focus on on-device AI processing, real-time computer vision, and energy-efficient AI chip design, essential for enhancing user experiences in smart devices. The skills gained in optimizing AI for hardware translate to advancing Apple’s innovations in autonomous systems, smart wearables, and AI chip development.
Popular Industry Positions
Embedded Systems Engineer
Optimize AI integration for energy-efficient, on-device machine learning.
Computer Vision Engineer
Implement real-time AI models for smart cameras, AR, and wearable tech.
AI Hardware Engineer
Develop AI-optimized processors and edge computing systems for Apple devices.
Tracks
Track 1
AI Vision and Detection on Smart Glass
Learners will develop AI-powered vision systems for smart glasses, enabling real-time object detection and interaction in wearable technology.
Understand the fundamentals of AI hardware acceleration and edge computing for smart devices.
Learn how to deploy real-time AI models for object detection on wearable hardware.
Explore optimization techniques for AI models running on low-power devices.
Gain hands-on experience with tools like Python, TensorFlow, OpenCV, and Edge TPU.
Build a prototype smart glasses system that performs AI-driven object detection in real time.
Track 2
AI-Driven Chip Design Optimization
Learners will explore how AI can optimize semiconductor design, reducing development cycles and improving energy efficiency.
Learn the principles of AI-powered design automation in semiconductor engineering.
Use reinforcement learning and genetic algorithms to optimize chip architectures.
Work with industry-standard tools like Python, TensorFlow, PyTorch, and Cadence.
Understand trade-offs in power, performance, and cost when designing AI-optimized chips.
Develop an AI-driven framework that generates and evaluates optimized chip configurations.
Track 3
AI in Autonomous Systems and Robotics
Learners will design and implement AI-driven control systems for self-driving and robotic applications.
Learn the fundamentals of autonomous navigation, sensor fusion, and AI-based control algorithms.
Explore AI-powered lane detection, obstacle avoidance, and real-time decision-making in robotics.
Gain hands-on experience with tools like Python, ROS (Robot Operating System), OpenCV, and NVIDIA Jetson.
Develop an AI-powered robotic system that autonomously navigates a track and avoids obstacles.
Implement real-world AI integration for autonomous machines and intelligent transportation systems.
PBL Journey
Online PBL Projects meet once a week for 8 weeks, and follow the research project format. Participants will meet the project lead, learn the conventions of the field and familiarize themselves with the tracks, then spend the middle portion of their time collaborating to develop their research.
At the end, participants will present their final project and receive feedback, with the opportunity to extend their timeline and develop the project in greater depth.
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Project Team
Our Academic Team plays a vital role in your PBL journey at Blended Learning. We are dedicated to enhancing your learning experience and ensuring your academic success. Our team consists of three distinct roles, each with a specific focus to support your Research Guidance, Project Progress, and Personal Growth.
Project Lead
Providing Industry and Research Guidance
MIT AI & Hardware Researcher and Industry Expert
He conducted research at MIT, focusing on integrated photonic devices for deep brain neuroimaging, biosensing, and AI-driven healthcare applications. His work explores the intersection of machine learning, optical design, and sensor technology. He has authored 14+ journal publications, including in Nature, ACS Sensors, and APL, along with 5 patents and 10 conference proceedings. With 4+ years of industry experience at a BigTech company, he is developing next-generation wearable AI devices for AR/VR technology. His goal in leading this PBL track is to equip students with the most cutting-edge industry skills, emphasizing: AI isn’t replacing jobs—it’s redefining them.
Academic Advisor
Tracking Your Project Development
The Academic Advisor is dedicated to your project completion success. They manage the progress of your PBL, guiding team formation, facilitating group discussions, and resolving conflicts. Additionally, the Academic Advisor ensures team member contributions are on track and provides logistical support, including attendance tracking, hosting recitation sessions, managing research support requests, and conducting student evaluations at the end of the PBL.
From Our Students
"After a night spent debugging, I suddenly discovered the program running perfectly. In that triumphant moment, you realize your true capability and success. The exhaustion fades, replaced by the thrill of knowing your skills and persistence led to this achievement, reaffirming your potential."
Nicole Y.
National University of Singapore
B.S. Economics
FAQs
What is the learning format of a PBL?
All PBLs are offered in an 8-week online format that begins with an orientation followed by subject setup overview of the different tracks. The majority of the session time is dedicated to project development, with a final presentation at the culmination of the 8 weeks. Many PBLs are also offered bi-annually in an on-campus format that consists of daily in-person meetings.
How long does each PBL cohort last？
One round of the Online PBL cohort lasts 8 weeks, preceded bys a pre-PBL orientation week. Each On-Campus PBL usually has 8 in person meetings, with intensive classroom education and collaboration. This means the biggest difference between online and on-campus PBLs is time participants have in between meetings.
How can I be more academically prepared before the PBL starts?
Review the Blended Learning Insights sent by the Academic Advisor and familiarize yourself with the project topic and pre-learning materials. Ensure you have all necessary softwares and other resources needed for the PBL.
For each PBL cohort, will I work in teams? Are PBL team members self-selected or assigned?
Yes, you will work in teams for each round of the PBL Cohort. Each team has 3 to 6 participants, organized by the Academic Team. The Academic Advisor will organize groupings based on students' backgrounds, preferred track, and skills.
Can I work with the Project Lead on my project after the PBL ends?
Yes, with your AI + X Research Plan, you may request up to five PBL Research Extension meetings, where you work with the project lead to develop your project into a working manuscript. To schedule a PBL Research Extension meeting, talk to your Academic Advisor at the conclusion of your PBL.
What do I receive at the end of the PBL?
At the conclusion of the PBL cohort, you can request a PBL Evaluation Report which summarizes the PBL content, the hours you spent, the track you chose, and includes a recommendation letter from the Project Lead (for eligible participants who completed the project successfully).
Is attendance mandatory for PBL Live Sessions and Recitation Sessions?
Yes, attendance is mandatory for both PBL Live Sessions and Recitation Sessions. Participants with three or more unexcused absences forfeit their eligibility for a PBL Evaluation Report.
Do I need to have my camera on during online PBL Live Sessions?
Yes, you must have your camera on during online PBL Live Sessions. Participants with cameras off will be marked as absent. This is meant to encourage active engagement and participation in meetings.
