Embedding AI at the Factory Edge

Balance the computing trade-offs of high fidelity edge computing with neural networks and machine learning models.

May 30, 2021

Acoustic Monitoring

Explore the history of large-format 3D printing and how to design your parts for best results on these machines. Guest: Marques Franklin, GE Additive Customer Success Technical Account Manager.

Assembly Line

How Edge Analytics Can Help Manufacturers Overcome Obstacles Associated with More Equipment Data

Date: May 26, 2021
Author: Martin Thunman

Big data is transforming a variety of sectors, ushering them into the era of Industry 4.0. However, having access to raw data and knowing what to do with it are at completely different ends of the digitalization spectrum. To help manufacturers understand, and overcome, some of the challenges associated with smart manufacturing, Martin Thunman, CEO and co-founder of leading low-code platform for streaming analytics, automation and integration for industrial IoT, Crosser shares his insight.

SLAM for the real world

Date: May 28, 2021
Author: Owen Nicholson

To take the next leap forward, the robotics industry needs software that is reliable and effective in the real-world, yet flexible and cost effective to integrate into a wider range of robot platforms and optimized to make efficient use of limited compute, power and memory resources. Creating ‘commercial-grade’ software that is robust enough to be deployed in thousands of robots in the real world, at prices that make that scale achievable, is the next challenge for the industry.

Tree Model Quantization for Embedded Machine Learning Applications

Date: May 28, 2021
Author: Leslie J. Schradin

Compressed tree-based models are useful models to consider for embedded machine learning applications, in particular with the compression technique: quantization. Quantization can compress models by significant amounts with a trade-off of slight loss in model fidelity, allowing more room on the device for other programs.

The realities of developing embedded neural networks

Date: May 28, 2021
Author: Tony King-Smith

With any embedded software destined for deployment in volume production, an enormous amount of effort goes into the code once the implementation of its core functionality has been completed and verified. This optimization phase is all about minimizing memory, CPU and other resources needed so that as much as possible of the software functionality is preserved, while the resources needed to execute it are reduced to the absolute minimum possible.

This process of creating embedded software from lab-based algorithms enables production engineers to cost-engineer software functionality into a mass-production ready form, requiring far cheaper, less capable chips and hardware than the massive compute datacenter used to develop it. However, it usually requires the functionality to be frozen from the beginning, with code modifications only done to improve the way the algorithms themselves are executed. For most software, that is fine: indeed, it enables a rigorous verification methodology to be used to ensure the embedding process retains all the functionality needed.

However, when embedding NN-based AI algorithms, that can be a major problem. Why? Because by freezing the functionality from the beginning, you are removing one of the main ways in which the execution can be optimized.

Surge Demand

Warehouse automation is being deployed at a rapid pace due to labor shortages. AI can now beat you at crosswords.

Exponential Industry