We address the problem of optimizing logic-level sequential circuits for low power. We present a powerful sequential logic optimization method that is based on selectively precomputing the output logic values of the circuit one clock cycle before they are required, and using the precomputed values to reduce internal switching activity in the succeeding clock cycle. We present two different precomputation architectures which exploit this observation. The primary optimization step is the synthesis of the precomputation logic, which computes the output values for a subset of input conditions. If the output values can be precomputed, the original logic circuit can be "turned off" in the next clock cycle and will have substantially reduced switching activity. The size of the precomputation logic determines the power dissipation reduction, area increase and delay increase relative to the original circuit. Given a logic-level sequential circuit, we present an automatic method of synthesizing precomputation logic so as to achieve maximal reductions in power dissipation. We present experimental results on various sequential circuits. Up to 75% reductions in average switching activity and power dissipation are possible with marginal increases in circuit area and delay.

Designing synthetic-aperture radar image formation systems can be challenging due to the numerous options of algorithms and devices that can be used. There are many SAR image formation algorithms, such as backprojection, matched-filter, polar format, Range–Doppler and chirp scaling algorithms. Each algorithm presents its own advantages and disadvantages considering efficiency and image quality; thus, we aim to introduce some of the most common SAR image formation algorithms and compare them based on these two aspects. Depending on the requisites of each individual system and implementation, there are many device options to choose from, for instance, FPGAs, GPUs, CPUs, many-core CPUs, and microcontrollers. We present a review of the state of the art of SAR imaging systems implementations. We also compare such implementations in terms of power consumption, execution time, and image quality for the different algorithms used.

The main contribution of this paper is an exact common subexpression elimination algorithm for the optimum sharing of partial terms in multiple constant multiplications (MCMs). We model this problem as a Boolean network that covers all possible partial terms that may be used to generate the set of coefficients in the MCM instance. We cast this problem into a 0-1 integer linear programming (ILP) by requiring that the single output of this network is asserted while minimizing the number of gates representing operations in the MCM implementation that evaluate to one. A satisfiability (SAT)-based 0-1 ILP solver is used to obtain the exact solution. We argue that for many real problems, the size of the problem is within the capabilities of current SAT solvers. Because performance is often a primary design parameter, we describe how this algorithm can be modified to target the minimum area solution under a user-specified delay constraint. Additionally, we propose an approximate algorithm based on the exact approach with extremely competitive results. We have applied these algorithms on the design of digital filters and present a comprehensive set of results that evaluate ours and existing approximation schemes against exact solutions under different number representations and using different SAT solvers.

Recently developed methods for power estimation have primarily focused on combinational logic. We present a framework for the efficient and accurate estimation of average power dissipation in sequential circuits. Switching activity is the primary cause of power dissipation in CMOS circuits. Accurate switching activity estimation for sequential circuits is considerably more difficult than that for combinational circuits, because the probability of the circuit being in each of its possible states has to be calculated. The Chapman-Kolmogorov equations can be used to compute the exact state probabilities in steady state. However, this method requires the solution of a linear system of equations of size 2/sup N/ where N is the number of flip-flops in the machine. We describe a comprehensive framework for exact and approximate switching activity estimation in a sequential circuit. The basic computation step is the solution of a nonlinear system of equations which is derived directly from a logic realization of the sequential machine. Increasing the number of variables or the number of equations in the system results in increased accuracy. For a wide variety of examples, we show that the approximation scheme is within 1-3% of the exact method, but is orders of magnitude faster for large circuits. Previous sequential switching activity estimation methods can have significantly greater inaccuracies.

This article provides a comprehensive survey of pioneer and state-of-the-art localization algorithms based on the mobility of the network. The basic concepts of the localization task in a wireless sensor network are revisited and the most common techniques suitable for random mobility are reviewed. This survey compiles and discusses the most relevant algorithms regarding localization in mobile networks, focusing on scenarios where nodes have no control over their mobility and hardware restrictions are imposed, including recent advances in learning-based solutions. It focuses on presenting techniques that do not rely on human intervention or a specialized field configuration. This unpredictability brings challenges that are not present in a static network nor in a mobile network built upon robotic entities. The basis for theoretical concepts of localization algorithms is gathered and organized in a comprehensive way, so researchers may quickly get started in this field. Significant aspects addressed throughout the article, such as mobility pattern, range scheme, and computational complexity are organized and discussed as well as performance data for quantitative analysis alongside related ponderations. This survey concludes by pointing out current and future trends.