“SVM”

An Analog-to-Information VGA Image Sensor Architecture for Support Vector Machine on Compressive Measurements

Abstract: This work presents a compact VGA (480 × 640) CMOS Image Sensor (CIS) architecture with dedicated end-of-column Compressive Sensing (CS) scheme allowing embedded object recognition. The architecture takes advantage of a low-footprint pseudo-random data mixing circuit and a first order incremental Sigma-Delta (ΣΔ) Analog to Digital Converter (ADC) to extract compressed features.

Exploring Hierarchical Machine Learning for Hardware-Limited Multi-Class Inference on Compressed Measurements

Abstract: This paper explores hierarchical clustering methods to learn a hierarchical multi-class classifier on compressed measurements in the context of highly constrained hardware (e.g., always-on ultra low power vision systems). In contrast to the popular multi-class classification approaches based on multiple binary classifiers (i.