‘Industrial manufacturing has entered a new stage with IoT and 5G tech’

‘Industrial manufacturing has entered a new stage with IoT and 5G tech’

Peng Zhang’s studies have taken him from China to Ireland, where he is now trying to improve AI technology for the smart factories of the future.

Peng Zhang studied mechanical engineering at the Harbin Institute of Technology in China, before going to France and completing a master’s degree in advanced robotics.

He came to Ireland for his PhD research in 2020 and is now based at Shannon University of Technology in Athlone. His work is funded by Confirm, the Science Foundation Ireland research center for smart manufacturing.

‘I work on optimizing distributed deep neural network deployment in edge environments’

Tell us about the research you are currently working on.

My research work is related to smart manufacturing. Industrial manufacturing has entered a new stage with the rapid development of the Internet of Things and 5G technology.

However, the industrial environment is different from the cloud environment. This is called the mist or edge environment. Compared to cloud computing, edge computing devices are closer to the factory. This can drastically reduce latency caused by data transfers between computing devices and the factory. At the same time, it can improve the protection of data privacy.

I work on optimizing distributed deep neural network deployment in edge environments. Currently I am researching pipeline parallelism, which is an improvement of model parallelism. Model parallelism addresses the problem that a large deep neural network model cannot be stored in a single device such as a GPU.

My research includes analyzing the performance of pipeline parallelism under different settings and proposing the methods to improve the pipeline parallelism performance.

In your opinion, why is your research important?

As we all know, manufacturing environments require strict response times to ensure the correctness of the manufacturing process. And moving the decision functions from the cloud to the edge is a promising scheme.

However, since edge devices are characterized by severe resource constraints with dynamic and heterogeneous nature, training deep learning models or deploying trained models over these distributed and heterogeneous resources remains an open challenge. And what I do is develop a solution to these challenges.

What inspired you to become a researcher in this field?

Based on my undergraduate and master’s studies in the field of machinery, especially robotics, I developed a strong interest in industrial robots and humanoid robots.

At the same time, my master’s degree study enabled me to come into contact with the research field of artificial intelligence. And intelligent algorithms and systems are of great importance in robotics. Therefore, I chose to pursue research in computer science to improve my understanding of this field.

What are some of the biggest challenges or misconceptions you face as a researcher in your field?

The biggest challenge I have encountered is the Covid-19 pandemic. I started my research at the beginning of 2020 and we had to isolate at home during that year. Therefore, it was a challenge for me to balance research work and personal life during that time.

As far as my research work is concerned, the heterogeneous fringe environment is another challenge. Heterogeneous refers to the different configurations in the edge environment. For example, the different types of accelerators, different types of models, etc. This heterogeneous edge environment makes deployment in edge environments a major challenge.

Do you think public engagement with science has changed in recent years?

Yes, I think public engagement with science has changed during the pandemic. People now pay more attention to technological developments.

In addition, the balance between technological development and nature is becoming a subject of increasing concern.

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