Pipelined digital SAR azimuth correlator using hybrid FFT-transversal filter

A synthetic aperture radar system (SAR) having a range correlator is provided with a hybrid azimuth correlator which utilizes a block-pipe-lined fast Fourier transform (FFT). The correlator has a predetermined FFT transform size with delay elements for delaying SAR range correlated data so as to embed in the Fourier transform operation a corner-turning function as the range correlated SAR data is converted from the time domain to a frequency domain. The azimuth correlator is comprised of a transversal filter to receive the SAR data in the frequency domain, a generator for range migration compensation and azimuth reference functions, and an azimuth reference multiplier for correlation of the SAR data. Following the transversal filter is a block-pipelined inverse FFT used to restore azimuth correlated data in the frequency domain to the time domain for imaging

Truncated Binary Multipliers with minimum Mean Square Error: analytical characterization, circuit implementation and applications

In the wireless multimedia word, DSP systems are ubiquitous. DSP algorithms are computationally intensive and test the limits of battery life in portable device such as cell phones, hearing aids, MP3 players, digital video recorders and so on. Multiplication and squaring are the main operation in many signal processing algorithms (filtering, convolution, FFT, DCT, euclidean distance etc.), hence efficient parallel multipliers are desirable. A full-width digital nxn bits multiplier computes the 2n bits output as a weighted sum of partial products. A multiplier with the output represented on n bits output is useful, as example, in DSP datapaths which saves the output in the same n bits registers of the input. Note that the truncated multipliers are useful not only for DSP but also for digital, computational intensive, ASICs where the bit-widths at the output of the arithmetic blocks are chosen on the basis of system-related accuracy issues. Hence 2n bits of precision at the multiplier output are very often more than required. A truncated multiplier is an nxn multiplier with n bits output. Since in a truncated multiplier the n less-significant bits of the full-width product are discarded, some of the partial products are removed and replaced by a suitable compensation function, to trade-off accuracy with hardware cost. Several techniques have been proposed in the Literature following this basic idea. The difference between the various circuits is in the choice and the implementation of the compensation circuit. The correction techniques proposed in the Literature are obtained through exhaustive search. This means that the results are only available for small n values and that the proposed approach are not extendable to greater bit widths. Furthermore the analytical characterization of the error is not possible. In this dissertation an innovative solution for the design and characterization of truncated multipliers is presented. The proposed circuits are based on the analytical calculation of the error of the truncated multiplier. This approach allows to have the description of a multiplier characterized by a minimum mean square error which gives a fast and low power VLSI implementation. Furthermore the analytical approach yields to a closed form expression of the mean square error and maximum absolute error for the proposed truncated multipliers. In this way the a priori knowledge of the output error is available. The errors are known for every bit width of the multiplier and it is also possible to decide, for a given bit width, which correction circuit has to be used in order to obtain a certain error. This analytical relation between the error and the parameters of hardware implementation is extremely important for the digital designer, since now it is possible to select the suitable implementation as a function of the desired accuracy. Proposed truncated multipliers overcome the previously proposed truncated multipliers since provide lower error, lower power dissipation, lower area occupation and also provide higher working frequency. The circuits are also easily implemented and allow an automatic HDL description as a function of bit width and desired error. The complete description of the errors for the truncated multipliers allows the use of these circuits as building blocks for more complex systems. It will be shown how the proposed multiplier can be used to design low area occupation FIR filters and an efficient PI temperature controller

Design of Energy-Efficient Approximate Arithmetic Circuits

Energy consumption has become one of the most critical design challenges in integrated circuit design. Arithmetic computing circuits, in particular array-based arithmetic computing circuits such as adders, multipliers, squarers, have been widely used. In many cases, array-based arithmetic computing circuits consume a significant amount of energy in a chip design. Hence, reduction of energy consumption of array-based arithmetic computing circuits is an important design consideration. To this end, designing low-power arithmetic circuits by intelligently trading off processing precision for energy saving in error-resilient applications such as DSP, machine learning and neuromorphic circuits provides a promising solution to the energy dissipation challenge of such systems. To solve the chip’s energy problem, especially for those applications with inherent error resilience, array-based approximate arithmetic computing (AAAC) circuits that produce errors while having improved energy efficiency have been proposed. Specifically, a number of approximate adders, multipliers and squarers have been presented in the literature. However, the chief limitation of these designs is their un-optimized processing accuracy, which is largely due to the current lack of systemic guidance for array-based AAAC circuit design pertaining to optimal tradeoffs between error, energy and area overhead. Therefore, in this research, our first contribution is to propose a general model for approximate array-based approximate arithmetic computing to guide the minimization of processing error. As part of this model, the Error Compensation Unit (ECU) is identified as a key building block for a wide range of AAAC circuits. We develop theoretical analysis geared towards addressing two critical design problems of the ECU, namely, determination of optimal error compensation values and identification of the optimal error compensation scheme. We demonstrate how this general AAAC model can be leveraged to derive practical design insights that may lead to optimal tradeoffs between accuracy, energy dissipation and area overhead. To further minimize energy consumption, delay and area of AAAC circuits, we perform ECU logic simplification by introducing don't cares. By applying the proposed model, we propose an approximate 16x16 fixed-width Booth multiplier that consumes 44.85% and 28.33% less energy and area compared with theoretically the most accurate fixed-width Booth multiplier when implemented using a 90nm CMOS standard cell library. Furthermore, it reduces average error, max error and mean square error by 11.11%, 28.11% and 25.00%, respectively, when compared with the best reported approximate Booth multiplier and outperforms the best reported approximate design significantly by 19.10% in terms of the energy-delay-mean square error product (EDE_(ms)). Using the same approach, significant energy consumption, area and error reduction is achieved for a squarer unit, with more than 20.00% EDE_(ms) reduction over existing fixed-width squarer designs. To further reduce error and cost by utilizing extra signatures and don't cares, we demonstrate a 16-bit fixed-width squarer that improves the energy-delay-max error (EDE_(max)) by 15.81%

A Survey on Approximate Multiplier Designs for Energy Efficiency: From Algorithms to Circuits

Given the stringent requirements of energy efficiency for Internet-of-Things edge devices, approximate multipliers, as a basic component of many processors and accelerators, have been constantly proposed and studied for decades, especially in error-resilient applications. The computation error and energy efficiency largely depend on how and where the approximation is introduced into a design. Thus, this article aims to provide a comprehensive review of the approximation techniques in multiplier designs ranging from algorithms and architectures to circuits. We have implemented representative approximate multiplier designs in each category to understand the impact of the design techniques on accuracy and efficiency. The designs can then be effectively deployed in high-level applications, such as machine learning, to gain energy efficiency at the cost of slight accuracy loss.Comment: 38 pages, 37 figure

Frame Theory for Signal Processing in Psychoacoustics

This review chapter aims to strengthen the link between frame theory and signal processing tasks in psychoacoustics. On the one side, the basic concepts of frame theory are presented and some proofs are provided to explain those concepts in some detail. The goal is to reveal to hearing scientists how this mathematical theory could be relevant for their research. In particular, we focus on frame theory in a filter bank approach, which is probably the most relevant view-point for audio signal processing. On the other side, basic psychoacoustic concepts are presented to stimulate mathematicians to apply their knowledge in this field

Abatement costs for agricultural nitrogen and phosphorus loads: a case study of crop farming in south-western Finland

Designing efficient agri-environmental policies for agricultural nutrient load reductions calls for information on the costs of emission reduction measures. This study develops an empirical framework for estimating abatement costs for nutrient loading from agricultural land. Nitrogen abatement costs and the phosphorus load reductions associated with nitrogen abatement are derived for crop farming in south-western Finland. The model is used to evaluate the effect of the Common Agricultural Policy reform currently underway on nutrient abatement costs. Results indicate that an efficiently designed policy aimed at a 50% reduction in agricultural nitrogen load would cost € 48 to € 35 million, or € 3756 to € 2752 per farm

A Study of Linear Approximation Techniques for SAR Azimuth Processing

The application of the step transform subarray processing techniques to synthetic aperture radar (SAR) was studied. The subarray technique permits the application of efficient digital transform computational techniques such as the fast Fourier transform to be applied while offering an effective tool for range migration compensation. Range migration compensation is applied at the subarray level, and with the subarray size based on worst case range migration conditions, a minimum control system is achieved. A baseline processor was designed for a four-look SAR system covering approximately 4096 by 4096 SAR sample field every 2.5 seconds. Implementation of the baseline system was projected using advanced low power technologies. A 20 swath is implemented with approximately 1000 circuits having a power dissipation of from 70 to 195 watts. The baseline batch step transform processor is compared to a continuous strip processor, and variations of the baseline are developed for a wide range of SAR parameters

Hybrid computer Monte-Carlo techniques

Hybrid analog-digital computer systems for Monte Carlo method application