Presentation
A Retina-Inspired Pathway to Real-Time Motion Prediction inside Image Sensors for Extreme-Edge Intelligence
DescriptionEnergy-efficient, real-time motion prediction (MP) enables autonomous agents to swiftly track objects and adapt to unexpected trajectory changes, essential for tasks like escape, attack, and tracking. We present a retina-inspired neuromorphic framework capable of real-time, energy-efficient MP within image sensor pixels. Our hardware-algorithm co-design utilizes a biphasic filter, spike adder, non-linear circuit, and a 2D array for multi-directional MP, implemented on GlobalFoundries 22nm FDSOI technology. A 3D Cu-Cu hybrid bonding approach enables design compactness, reducing area and routing complexity. Validated on Berkeley DeepDrive dataset, the model provides efficient, low-latency MP for decision-making scenarios that depend on predictive visual computation.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby