The Information Processing Factory (IPF) project

IPF aims to show how self-awareness can be achieved across multiple abstraction levels, and discuss new research challenges.

It represents a paradigm shift in platform design by envisioning the move towards a consequent platform-centric design in which the combination of self-organizing learning and formal reactive methods guarantee the  applicability of such cyber-physical systems in safety-critical and high-availability applications.

IPF project is an international collaboration among the following universities:

University of California, Irvine


Technische Universität München


Technische Universität Braunschweig


Principal Investigators :
Nikil Dutt 
Fadi Kurdahi

Principal Investigators :
Andreas Herkersdorf 

Principal Investigators :
Rolf Ernst 


The key methodological innovation is a new approach to control platform dynamics at runtime by combining selforganizing machine learning techniques with formal reactive control methods providing platform worst-case real-time and safety guarantees, as embodied in our IPF paradigm, instead of using a single, static, operating point determined at design time. The authors are involved in parallel projects targeting self-aware vehicles for autonomous driving which will provide use cases for IPF research.

Why Factory ?

We use the metaphor of an Information Processing Factory (IPF) to draw similarities between microelectronics systems and factories as follows: in a factory, all the components must adapt to the current workload. This includes logistics of supplies such as material, energy, water and waste, the manufacturing equipment, the transport, the control and infrastructure (such as heating, air-conditioning, illumination). This adaptation cannot be done offline and must instead be done in real time without interrupting the baseline operations.

Future microelectronic systems (e.g., MPSoCs) should operate in a similar manner. Parallel to the baseline operation of the system, a platform operation layer (POL) is continuously monitoring and controlling the performance and health status of the system. This layer monitors the system using a network of on-chip infrastructure that senses cross-layer metrics such as temperature, aging, energy, performance, reliability, and security and accordingly orchestrates the operations of different system components such as application, storage, I/O and even non-electronic functions (e.g. micromechanics, microfluidics, etc.). 

This IPF analogy implies that clusters of componentspecific, uncorrelated control occurrences are unable to cope with the complexity coordinating the operations of large scale systems with multi-criteria objective functions. Similarly, a centralized controller model is also inadequate in this case because it cannot scale. Our goal is to demonstrate that a hybrid hierarchical approach, sporting as much modularity as possible and as much centralized as necessary, is a much more effective means of achieving the desired goal while maintaining cost efficiency, low overhead, and scalability

This work is being financially supported by:

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