How Tiny Can Analog Filterbank Features Be Made for Ultra-low-power On-device Keyword Spotting
EDGE AI FOUNDATION via YouTube
Learn AI, Data Science & Business — Earn Certificates That Get You Hired
The Most Addictive Python and SQL Courses
Overview
Google, IBM & Meta Certificates – 40% Off
One plan covers every Professional Certificate on Coursera.
Unlock All Certificates
Watch a 16-minute research symposium presentation exploring power optimization techniques for analog filterbank features in ultra-low-power keyword spotting systems. Learn how Columbia University PhD student Subhajit RAY investigates the architectural parameters of analog filterbanks to achieve significant power reductions while maintaining high accuracy. Discover the comparison between analog and digital approaches, understand the fundamentals of analog audio feature extraction, and explore how careful parameter selection led to a 33.6x power reduction with only a minimal 1.8% accuracy loss in a 10-word keyword spotting system. Follow along through key topics including introduction, motivation, analog vs digital comparison, analog audio feature extraction methods, power saving strategies, and a comprehensive summary of findings.
Syllabus
Introduction
Motivation
Analog vs Digital
Analog Audio Feature Extraction
Power Savings
Summary
Taught by
EDGE AI FOUNDATION