tinyML Talks: Practical application of tinyML in battery powered anomaly sensors for predictive maintenance of industrial assets & Pushing the limits of RNN Compression using Kronecker Products

Date

August 18, 2020

Location

Virtual

Contact us

Discussion

Schedule

Timezone: PDT

Practical application of tinyML in battery powered anomaly sensors for predictive maintenance of industrial assets

Mark STUBBS, Principal Architect

Shoreline IoT

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.

Mark STUBBS, Principal Architect

Shoreline IoT

Timezone: PDT

Pushing the limits of RNN Compression using Kronecker Products

Urmish THAKKER, Principal Engineer

SambaNova Systems Inc

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). We present experimental results that demonstrate compression of a large language model with LSTM layers of size 25 MB by 25x with 1.4% loss in perplexity score using DKP and show that it outperforms other traditional compression technique.

Urmish THAKKER, Principal Engineer

SambaNova Systems Inc

Urmish is a Principal Engineer at SambaNova Systems. Previously, he was a Senior Research Engineer at Arm’s ML Research Lab where he worked on efficient execution of NN on Arm devices. His worked spanned both algorithms and hardware for ML. He has published 20+ papers and patents on topics like model compression (pruning, quantization, low rank decomposition, structured matrices, NAS and conditional computation), efficient libraries for NN and hardware and accelerators for NLP Applications.

Along with extensive experience in the field of machine learning, Urmish has also worked on performance modeling, design and verification of CPUs and memory controllers at Texas Instruments, AMD and Broadcom.

He holds a Master’s from University of Wisconsin Madison, USA and Bachelor’s from Birla Institute of Technology and Science, India.

Schedule subject to change without notice.