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Predicting Differential Loss at the Edge: Lightweight ML for Real-Time Test Intelligence
Inspiration In high-throughput production environments, every sensor reading tells a story. Test systems continuously record Pressure , Temperature , and Differential Loss (DL) across thousands of cycles, but much of this data remains passive, observed but not interpreted. We set out to change that by deploying machine learning directly at the edge on a BeagleBone Black board. The goal was not anomaly detection, but live inference : to compute what the ideal DL should be (

Alisha Bhale
Oct 20, 20253 min read
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