Research Projects

HOCOS - Current Research Projects

Cryptographic circuits are employed in mobile and embedded systems to protect sensitive information from unauthorized access and manipulation. Fault attacks circumvent the protection by injecting faults into the hardware implementation of the cryptographic function, thus manipulating the calculation in a controlled manner and allowing the attacker to derive protected data such as secret keys.

The Algebraic Fault Attacks project focuses on the class of algebraic fault attacks, where the information used for cryptanalysis is represented by systems of polynomials.

Benchmarks for algebraic fault attacks

We are working on creating a comprehensive set of benchmarks for algebraic fault attacks. These will be published here as soon as they are available.

Fault Attack Benchmarks for Small Scale AES




Project is maintained by University of Passau

Memristive devices offer enormous advantages for non-volatile memories and neuromorphic computing, but there is a rising interest in using memristive technologies for security applications. Project MemCrypto aims at development and investigation of memristive cryptographic implementations, assessment and improvement of their security against physical attacks. This work focuses on combinational and sequential realizations of complete cryptographic circuits and complements earlier research on memristive physical unclonable functions and random number generators.

please visit project page

This project aims at developing methods to realize low-cost and power-efficient hardware circuits for near-sensor computing following the Stochastic Computing paradigm. Stochastic computing provides extremely compact, error-tolerant and low-power implementations of complex functions, but at the expense of longer computation times and some degree of inaccuracy. This makes stochastic circuits (SCs) especially attractive for near-sensor computing, where the processed sensor data are inaccurate anyway and computations tend to occur infrequently. A special focus of this project will be the SC realization of neural networks (NNs) used for classification tasks, from lightweight NNs to fully-fledged convolutional NNs for deep learning.

please visit project page

Test quality, defined as the absence of test escapes (defective circuits that had passed post-manufacturing test), is the ultimate target of testing. Customers apply system-level test (SLT) to circuits that already have been tested post-fabrication and reportedly identify test escapes. The objective of this project is to understand the nature of such hard-to-detect failures. Establishing a better understanding of SLT and making it more effective and efficient could drastically improve the economy of circuit design and manufacturing.

Please visit gs-imtr project site

CA - Current Research Projects

Project Description

Computer systems have reached a point where significant improvements in computational performance and energy efficiency have become very hard to achieve. The main reason is a power and efficiency wall CMOS technology is facing. Physical limitations such as high power densities and a variety of reliability degradations now enforce larger design margins which reduce efficiency.

Approximate Computing trades off precision against power, energy, storage, bandwidth or performance, and can be applied to hardware, software and algorithms. It enables much more efficient computing by providing additional, adjustable design and runtime parameters to find Pareto optimal solutions. However, its application is still rather limited and a significant extension of the scope of applications is required, including applications that are not necessarily inherently error-tolerant.

The ACCROSS project will tackle this challenge with a cross-layer approach to analysis and optimization, which considers the system stack from the application down to the hardware. At the higher levels, ACCROSS covers the analysis of applications from different computational problem classes, which will act as enablers for mainstream approximate computing. This includes the development of new methods for the analysis of approximation potentials in applications, the adaptation of existing applications to approximation and the quantification of efficiency gains. Moreover, new methods for combining suitable approximation techniques at different system layers during runtime will be provided to maximize efficiency with respect to performance and energy. New error metrics and methods for lightweight runtime monitoring of accuracy will be developed to ensure the usefulness of the targeted applications. At the lower levels, ACCROSS covers the systematic evaluation of the impact of removing design margins which will lead to approximate behavior and improved efficiency. Abstract but accurate models linking the hardware and software will be provided, enabling designers to accurately quantify the error and efficiency impact of approximation across the system stack.

ES - Current Research Projects

Technology scaling makes it possible to implement systems with hundreds of processing cores, and thousands in the future, on a single chip. The communication in such systems is enabled by Networks-on-Chips (NoCs). A downside of technology scaling is the increased susceptibility to failures emerging in NoC resources during operation. Ensuring reliable operation despite such failures degrades NoC performance and may even invalidate the performance benefits expected from scaling. Thus, it is not enough to analyze performance and reliability in isolation, as usually done. Instead, we research how both aspects can be treated together using the concept of performability and its analysis with Markov reward models. In addition to developing modelling and analysis techniques, we exemplify our methodology through application to compare various NoC topologies and fault-tolerant routing algorithms. We investigate how performability develops with scaling towards larger NoCs and explore the limits of scaling by determining the break-even failure rates under which scaling can achieve net performability increase.

To the top of the page