Formal Verification of an Iterative Low-Power x86 Floating-Point Multiplier with Redundant Feedback

We present the formal verification of a low-power x86 floating-point multiplier. The multiplier operates iteratively and feeds back intermediate results in redundant representation. It supports x87 and SSE instructions in various precisions and can block the issuing of new instructions. The design has been optimized for low-power operation and has not been constrained by the formal verification effort. Additional improvements for the implementation were identified through formal verification. The formal verification of the design also incorporates the implementation of clock-gating and control logic. The core of the verification effort was based on ACL2 theorem proving. Additionally, model checking has been used to verify some properties of the floating-point scheduler that are relevant for the correct operation of the unit.Comment: In Proceedings ACL2 2011, arXiv:1110.447

A Better Understanding of the Performance of Rate-1/2 Binary Turbo Codes that Use Odd-Even Interleavers

The effects of the odd-even constraint - as an interleaver design criterion - on the performance of rate-1/2 binary turbo codes are revisited. According to the current understanding, its adoption is favored because it makes the information bits be uniformly protected, each one by its own parity bit. In this paper, we provide instances that contradict this point of view suggesting for a different explanation of the constraint's behavior, in terms of distance spectrum

Evolution of oil droplets in a chemorobotic platform

Evolution, once the preserve of biology, has been widely emulated in software, while physically embodied systems that can evolve have been limited to electronic and robotic devices and have never been artificially implemented in populations of physically interacting chemical entities. Herein we present a liquid-handling robot built with the aim of investigating the properties of oil droplets as a function of composition via an automated evolutionary process. The robot makes the droplets by mixing four different compounds in different ratios and placing them in a Petri dish after which they are recorded using a camera and the behaviour of the droplets analysed using image recognition software to give a fitness value. In separate experiments, the fitness function discriminates based on movement, division and vibration over 21 cycles, giving successive fitness increases. Analysis and theoretical modelling of the data yields fitness landscapes analogous to the genotype–phenotype correlations found in biological evolution. , Trevor Hinkley, James Ward Taylor Kliment Yane