An area-efficient 2-D convolution implementation on FPGA for space applications

The 2-D Convolution is an algorithm widely used in image and video processing. Although its computation is simple, its implementation requires a high computational power and an intensive use of memory. Field Programmable Gate Arrays (FPGA) architectures were proposed to accelerate calculations of 2-D Convolution and the use of buffers implemented on FPGAs are used to avoid direct memory access. In this paper we present an implementation of the 2-D Convolution algorithm on a FPGA architecture designed to support this operation in space applications. This proposed solution dramatically decreases the area needed keeping good performance, making it appropriate for embedded systems in critical space application

Quantifying Shannon's Work Function for Cryptanalytic Attacks

Attacks on cryptographic systems are limited by the available computational resources. A theoretical understanding of these resource limitations is needed to evaluate the security of cryptographic primitives and procedures. This study uses an Attacker versus Environment game formalism based on computability logic to quantify Shannon's work function and evaluate resource use in cryptanalysis. A simple cost function is defined which allows to quantify a wide range of theoretical and real computational resources. With this approach the use of custom hardware, e.g., FPGA boards, in cryptanalysis can be analyzed. Applied to real cryptanalytic problems, it raises, for instance, the expectation that the computer time needed to break some simple 90 bit strong cryptographic primitives might theoretically be less than two years.Comment: 19 page

A dynamically reconfigurable pattern matcher for regular expressions on FPGA

In this article we describe how to expand a partially dynamic reconfig- urable pattern matcher for regular expressions presented in previous work by Di- vyasree and Rajashekar [2]. The resulting, extended, pattern matcher is fully dynamically reconfigurable. First, the design is adapted for use with parameterisable configurations, a method for Dynamic Circuit Specialization. Using parameteris- able configurations allows us to achieve the same area gains as the hand crafted reconfigurable design, with the benefit that parameterisable configurations can be applied automatically. This results in a design that is more easily adaptable to spe- cific applications and allows for an easier design exploration. Additionally, the pa- rameterisable configuration implementation is also generated automatically, which greatly reduces the design overhead of using dynamic reconfiguration. Secondly, we propose a number of expansions to the original design to overcome several limitations in the original design that constrain the dynamic reconfigurability of the pattern matcher. We propose two different solutions to dynamically change the character that is matched in a certain block. The resulting pattern matcher, after these changes, is fully dynamically reconfigurable, all aspects of the implemented regular expression can be changed at run-time

Janus II: a new generation application-driven computer for spin-system simulations

This paper describes the architecture, the development and the implementation of Janus II, a new generation application-driven number cruncher optimized for Monte Carlo simulations of spin systems (mainly spin glasses). This domain of computational physics is a recognized grand challenge of high-performance computing: the resources necessary to study in detail theoretical models that can make contact with experimental data are by far beyond those available using commodity computer systems. On the other hand, several specific features of the associated algorithms suggest that unconventional computer architectures, which can be implemented with available electronics technologies, may lead to order of magnitude increases in performance, reducing to acceptable values on human scales the time needed to carry out simulation campaigns that would take centuries on commercially available machines. Janus II is one such machine, recently developed and commissioned, that builds upon and improves on the successful JANUS machine, which has been used for physics since 2008 and is still in operation today. This paper describes in detail the motivations behind the project, the computational requirements, the architecture and the implementation of this new machine and compares its expected performances with those of currently available commercial systems.Comment: 28 pages, 6 figure

Eavesdropping on GSM: state-of-affairs

In the almost 20 years since GSM was deployed several security problems have been found, both in the protocols and in the - originally secret - cryptography. However, practical exploits of these weaknesses are complicated because of all the signal processing involved and have not been seen much outside of their use by law enforcement agencies. This could change due to recently developed open-source equipment and software that can capture and digitize signals from the GSM frequencies. This might make practical attacks against GSM much simpler to perform. Indeed, several claims have recently appeared in the media on successfully eavesdropping on GSM. When looking at these claims in depth the conclusion is often that more is claimed than what they are actually capable of. However, it is undeniable that these claims herald the possibilities to eavesdrop on GSM using publicly available equipment. This paper evaluates the claims and practical possibilities when it comes to eavesdropping on GSM, using relatively cheap hardware and open source initiatives which have generated many headlines over the past year. The basis of the paper is extensive experiments with the USRP (Universal Software Radio Peripheral) and software projects for this hardware.Comment: 5th Benelux Workshop on Information and System Security (WISSec 2010), November 201

Computer Architectures to Close the Loop in Real-time Optimization

© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other

Optimizing Scrubbing by Netlist Analysis for FPGA Configuration Bit Classification and Floorplanning

Existing scrubbing techniques for SEU mitigation on FPGAs do not guarantee an error-free operation after SEU recovering if the affected configuration bits do belong to feedback loops of the implemented circuits. In this paper, we a) provide a netlist-based circuit analysis technique to distinguish so-called critical configuration bits from essential bits in order to identify configuration bits which will need also state-restoring actions after a recovered SEU and which not. Furthermore, b) an alternative classification approach using fault injection is developed in order to compare both classification techniques. Moreover, c) we will propose a floorplanning approach for reducing the effective number of scrubbed frames and d), experimental results will give evidence that our optimization methodology not only allows to detect errors earlier but also to minimize the Mean-Time-To-Repair (MTTR) of a circuit considerably. In particular, we show that by using our approach, the MTTR for datapath-intensive circuits can be reduced by up to 48.5% in comparison to standard approaches