AkiSens

Adaptive AI-Based Real-Time Analysis of High-Frequency Sensor Data

Introduction

AkiSens is a joint research project of the Technical University of Munich and the IfTA GmbH, funded by the Bavarian Ministry of Economic Affairs, Regional Development and Energy in the context of the Bavarian Collaborative Research Program (BayVFP) and managed by the VDI/VDE Innovation + Technik GmbH.
The goal of this research project is to develop AI-based methods which are suitable for the real-time analysis of high-frequency sensor data.

Description

Nowadays, high-frequency environmental sensing is on the rise. More and more machines and devices, ranging from industrial facilities over power plants to consumer devices, have a large variety of integrated sensors, which enable them to collect data about their environment and state of operation. Especially high-frequency sensor signals, as generated, for example, by laser sensors with sampling rates of several million samples per second, are valuable for the operation of the considered devices.

In order to get meaningful information out of the raw sensor data, sophisticated and adaptive algorithms are needed to process and interpret the data and derive actions based on the analysis results. This adaptive sensor signal processing can be accomplished with AI- and machine learning-based methods, enabling the device to detect and classify certain situations and to react accordingly. Neural Network-based approaches have recently dominated the scoreboards of many machine learning applications, achieving state-of-the-art results.

However, due to their deeply-stacked architecture, typical implementations of Neural Network models, including Deep Fully-Connected Neural Networks, Recurrent Neural Networks, Long-Short-Term Memories, and Transformers, are not suitable for the application to time-critical real-time processing of high-frequency sensor signals as they are not capable of running several million inferences per second.
Thus, implementing AI methods in hardware and applying them to such high-frequent data remains challenging and requires further research.

Application

A prominent example considered in this research project is the processing of laser sensor data measured at two gears attached at both ends of a turbine shaft, as depicted in the picture above.
While the turbine shaft and attached gears rotate during operation, the two laser sensors detect the gears' position, resulting in square signals, each with a sampling rate of 4 MHz.
During the turbine's operation, the shaft can be subject to torsional oscillations and vibrations, which can damage the turbine at a certain level. Hence, it is desired to perform a highly accurate measurement of the torque and torsional oscillations using the two sensor signals.
However, the shape of the sensor signals can vary drastically under the influence of movements of the shaft, wear of the materials, and dirt particles, leading to the fact that, for example, simple thresholding techniques are insufficient. Therefore, more than classic and deterministic signal processing methods is required. AI-based methods can be exploited in this scenario to overcome the abovementioned issues and adapt to changing operating conditions.
In order to meet the real-time requirement, the signal processing should happen on an FPGA, which requires the considered models to be simple to implement and highly parallelizable.

Our Contributions

In the context of this research project, we develop and improve reservoir computing models as an adaptive AI method for high-frequent sensor signal processing on FPGAs. We aim to optimize the developed models for simplicity in their implementation on the one hand, as well as effectiveness on the other hand, resulting in a well-balanced compromise between throughput and accuracy.
In our research, we are dealing with the following topics:

  • Reservoir Computing and Echo State Networks as a feasible architecture for machine learning models with high-frequent inferences
  • Cellular Automata as simple reservoirs in Reservoir Computing models and the analytical analysis of their dynamics when described as linear mappings over Galois fields and rings
  • Implementation of such models on FPGAs

Open Student Work

Current Student Work

Hybrid Cellular Automata in Reservoir Computing

Keywords:
Cellular Automata, Reservoir Computing, ReCA
Short Description:
In this student work, hybrid CA-based reservoirs shall be analyzed, implemented, and evaluated.

Description

Introduction

Reservoir Computing (RC) is a promising and efficient computing framework that has been derived from neural networks and is especially suitable for time series data. In contrast to deep neural networks, which stack several layers one after the other, RC models only have three layers: an input layer, a reservoir, and an output layer.
Initially, the reservoir consists of several recurrently connected neurons and the model is called Echo State Networks (ESN). However, other reservoir implementations have been developed and employed since any dynamic system can serve as a reservoir within the RC framework.

Among the simplest types of dynamic systems are elementary Cellular Automata (CA). Acting on a regular grid of cells, each cell of a CA changes its state over time according to a simple predefined local rule. Despite their simplicity, CA can exhibit rich and complex behavior. Simple elementary CA have been shown to serve as the reservoir in RC models effectively. A modification of this approach is to use hybrid CA, in which not every cell adheres to the same rule.

Tasks

In this work, the tasks may include:

  • To implement a hybrid CA-based reservoir for RC models in Python using the TensorFlow framework
  • To evaluate the hybrid CA-based RC model and compare it with regular CA reservoirs as well as ESNs
  • Optional: To analyze the dynamic behavior of hybrid CA in terms of cycles and transients, and compare it with homogeneous (regular) CA

Prerequisites

In order to successfully carry out this work, you should:

  • be able to work independent and self-organized
  • have strong mathmatical skills; preferably have knowledge about finite fields / Galois fields
  • have good practice in programming with Python and the TensorFlow framework
  • have profound expertise in machine learning principles

Contact

Jonas Kantic | Room: N2118 | Tel: +49.89.289.22962 | E-Mail: jonas.kantic@tum.de

Supervisor:

Completed Student Work

Contact

frieder.jespers@nxp.com

Supervisor:

Jonas Kantic, Frieder Jespers (NXP Semiconductors)
- Frieder Jespers (NXP Semiconductors)

Student

Shereef Helal

Contact

Jonas Kantic

Room: N2118
Tel.: +49.89.289.22962
E-Mail: jonas.kantic@tum.de

 

Supervisor:

Jonas Kantic

Contact

Jonas Kantic

Room: N2118
Tel.: +49.89.289.22962
E-Mail: jonas.kantic@tum.de

 

Supervisor:

Jonas Kantic

Contact

Jonas Kantic

Chair of Integrated Systems

Office N2118, Building N1

Supervisor:

Jonas Kantic

Student

Yizhe Zhang

Contact

Email: fabian.legl@ifta.com

Supervisor:

Jonas Kantic, Fabian Legl (IfTA)
- Fabian Legl (IfTA)

Supervisor:

Jonas Kantic

Supervisor:

Jonas Kantic

Publications

(No documents in this view)

Preprints

J. Kantic and F. C. Legl and W. Stechele and J. Hermann. "ReLiCADA - Reservoir Computing using Linear Cellular Automata Design Algorithm" in arXiv, 2023, eprint: arXiv:2308.11522, DOI: https://doi.org/10.48550/arXiv.2308.11522.