"Practical application of tinyML in battery powered anomaly sensors for predictive maintenance of industrial assets"
CTO and Co-Founder
Shoreline IoT Inc.
Detecting anomalies in industrial equipment provides significant savings by preventing unplanned downtime and costly repairs due to unnoticed trends towards complete failure. Problems are detected and corrected early to attain maximum useful life from an asset. The combination of tinyML, low power wireless, integrated sensors, and IoT cloud enables a low cost and easy to install system to monitor industrial assets distributed throughout a factory. In this talk, we will present how tinyML is utilized for anomaly detection along with other sensor techniques to create a long life battery optimized solution for condition based maintenance in industry. We will also show a live demonstration of a tinyML-based end-to-end system solution.
"Pushing the limits of RNN Compression using Kronecker Products"
Senior Research Engineer
Arm/ ML Research Group
This talk gives an overview of our work in exploring Kronecker Products (KP) to compress sequence based neural networks. The talk is divided into two parts. In the first part we show that KP can compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods. This talk covers a quick tutorial on KP and the best methodology for using KP to compress IoT workloads. However when KP is applied to large Natural Language Processing tasks, it leads to significant accuracy loss (approx 26%). The second part of the talk addresses this issue. We show a way to recover accuracy otherwise lost when applying KP compression to large NLP tasks using a novel technique that we call doping. Doping is a process of adding an extremely sparse overlay matrix on top of the pre-defined KP structure. We call the resultant compression method doped kronecker product (DKP).