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The implementation of high-precision floating-point applications on reconfigurable hardware requires large multipliers. Full multipliers are the core of floating-point multipliers. Truncated multipliers, trading resources for a well-controlled accuracy degradation, are useful building blocks in situations where a full multiplier is not needed.This work studies the automated generation of such multipliers using the embedded multipliers and adders present in the DSP blocks of current FPGAs. The optimization of such multipliers is expressed as a tiling problem, where a tile represents a hardware multiplier, and super-tiles represent combinations of several hardware multipliers and adders, making efficient use of the DSP internal resources. This tiling technique is shown to adapt to full or truncated multipliers.It addresses arbitrary precisions including single, double but also the quadruple precision introduced by the IEEE-754-2008 standard and currently unsupported by processor hardware. An open-source implementation is provided in the FloPoCo project.

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Obfuscation is a mechanism used to hinder reverse engineering of programs. To cope with the large number of obfuscated programs, especially malware, reverse engineers automate the process of deobfuscation i.e. extracting information from obfuscated programs. Deobfuscation techniques target specific obfuscation transformations, which requires reverse engineers to manually identify the transformations used by a program, in what is known as metadata recovery attack. In this paper, we present Oedipus, a Python framework that uses machine learning classifiers viz., decision trees and naive Bayes, to automate metadata recovery attacks against obfuscated programs. We evaluated Oedipus' performance using two datasets totaling 1960 unobfuscated C programs, which were used to generate 11.075 programs obfuscated using 30 configurations of 6 different obfuscation transformations. Our results empirically show the feasibility of using machine learning to implement the metadata recovery attacks with classification accuracies of 100% in some cases.

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