Look-up Table Research Articles

Analysis of FPGA Architecture with Hybrid Logic Blocks Based on ULG and LUT

This paper analyzes the efficiency of different FPGA architectures consisting of Hybrid logic blocks (HLB) formed with Look-up Tables (LUTs) and Universal Logic Gates (ULGs). Pure LUT-based architectures induce area overhead and a longer delay. A ULG is designed to realize an efficient architecture compared to pure LUT-based architectures. Using this ULG and LUT in different proportions, HLBs are designed. The Field Programmable Gate Array (FPGA) architectures containing these proposed HLB structures are analyzed using various Microelectronics Centre of North Carolina (MCNC) benchmark circuits and compared with the performance of the existing designs. The result shows that the proposed architecture design can save a tremendous amount of time in overall flow and routing along with area improvements.

Rejection of Surface Clutter in Far Sidelobes for Airborne Radars

In airborne radar, false alarms caused by surface clutter returns through antenna sidelobes need to be suppressed to detect the target of interest. Sidelobe blanking is widely used to reject false alarms. Ideally, an auxiliary antenna radiation pattern should cover entire sidelobes of the main antenna radiation pattern. However, this may not always be satisfied, especially in the case of far sidelobes (FSL) of the main antenna, for various reasons. In this work, rejection techniques for false alarms caused by surface clutter returns through FSL are proposed. The distortion of the measured angle and velocity of the surface clutter are explained, and a measurement process for estimating the true location and velocity of the clutter is derived. Subsequently, two techniques for false alarm rejection are presented. The first technique is based on the look-up table (LUT) of FSL, which is robust but not practical due to the time and cost required to build a LUT with sufficient resolution. In the second technique, false alarms are rejected when they satisfy predefined criteria derived from their common characteristics, and flight test results confirm that false alarms can be effectively rejected by applying the second technique.

Refined semi-empirical models for soil moisture retrieval in spaceborne GNSS-Reflectometry: Evaluation across diverse land cover types

The satellite-based Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a promising technique for detecting surface soil moisture (SSM), which plays a pivotal role in various applications. Different approaches have been developed for SSM retrieval, including parametric semi-empirical models and non-parametric machine learning methods. This study, however, specifically focuses on constructing and evaluating semi-empirical fit models. To this end, the study compares the effectiveness of our enhanced pixel-by-pixel model, the land cover-based linear model, and the Reflectivity-Vegetation-Roughness (R-V-R) model across different land surface types, aiming to both evaluate their efficacy and identify potential limitations. In the assessment, various factors were taken into consideration, such as the correction for vegetation and roughness attenuation, fitting functions employed, and the utilization of a lookup table (LUT). The results of the evaluation showed variations in the performance of the retrieval models across different land cover types, highlighting the impact of the choice of fitting functions and attenuation correction strategies on the accuracy of soil moisture retrieval. The pixel-by-pixel model demonstrated the highest prediction accuracy, with an unbiased root mean square difference (ubRMSD) of 0.056 cm3/cm3 and a correlation coefficient of 0.896. By showcasing these outcomes, this research underscores the significance of accounting for surface conditions and integrating relevant data to enhance the accuracy of GNSS-R SSM retrieval, thereby contributing to the advancement of SSM monitoring methodologies.

A Novel NR-DA-Based ANN for SHEPWM in Cascaded Multilevel Inverters for Renewable Energy Applications

Harmonics is the major power quality problem caused by nonlinear devices, leading to malfunction or operational halt of the system. Low-order harmonics increase vibration and heat generation in motors. Therefore, controlling harmonics in the output waveform is the prime target for industrial applications to avoid economic loss. Selective harmonic elimination pulse width modulation (SHEPWM) is one of the techniques used for eliminating or minimizing selected harmonics in the output voltage waveform. This paper utilizes the Newton-Raphson method and Dragonfly Algorithms to calculate optimum switching angles for a Cascaded H-bridge Multilevel inverter (CHBMLI). The algorithms use non-linear equations to calculate the Switching Angles of MLI. The Dragonfly algorithm requires several iterations to reach an optimum solution. For complex problems, this algorithm becomes computationally expensive and time-consuming. A lookup table addresses the limitation by offline training an Artificial Neural Network (ANN) to generate the optimum switching angle for a given modulation index. Neural Fitting Tool in MATLAB software is used to train the ANN model. The simulation is performed using MATLAB SIMULINK software for both 5-level and 7-level CHBMLI configuration. The Dragonfly algorithm-based ANN achieves THD 8.84% when the modulation index (M) equals 0.8 for a 7-level inverter and THD 14.91% for a 5-level inverter and effectively minimizes third and fifth-order harmonics.

0.765-FJ/Bit/Search Content Addressable Memory Using Search Line Pre-Charge-Free (SLPF) Scheme

Content Addressable Memories (CAMs) are high-speed hardware lookup tables that are crucial in routing, packet classification, search, and other fast look-up applications. Despite having the advantage of high speed, they have limitations due to their power hungriness. This paper addresses the limitations of existing CAMs with two novel modifications in the proposed Search Line Pre-Charge-Free Low-Power-Content Addressable Memory (SLPFCAM) structure. The first novelty is their gated evaluation matching circuit and the second is a match line pre-charge controller. The proposed design reduces the energy consumption and improves the speed of the CAM by eliminating the SLprecharge phase in the CAM operation. The pre-charge controller prevents the attempt of match line pre-charge when it is not required. The design has been implemented using Cadence Virtuoso in a 90-nm CMOS technology with 1-V supply, and the post-layout simulation results show that the SLPFCAM makes significant improvements with a minimum of 83% less power consumption, a 91% less search delay, and the average energy consumption being 0.765 fJ/bit/search.

On Matrix Representation of Extension Field GF(pL) and Its Application in Vector Linear Network Coding.

For a finite field GF(pL) with prime p and L>1, one of the standard representations is L×L matrices over GF(p) so that the arithmetic of GF(pL) can be realized by the arithmetic among these matrices over GF(p). Based on the matrix representation of GF(pL), a conventional linear network coding scheme over GF(pL) can be transformed to an L-dimensional vector LNC scheme over GF(p). Recently, a few real implementations of coding schemes over GF(2L), such as the Reed-Solomon (RS) codes in the ISA-L library and the Cauchy-RS codes in the Longhair library, are built upon the classical result to achieve matrix representation, which focuses more on the structure of every individual matrix but does not shed light on the inherent correlation among matrices which corresponds to different elements. In this paper, we first generalize this classical result from over GF(2L) to over GF(pL) and paraphrase it from the perspective of matrices with different powers to make the inherent correlation among these matrices more transparent. Moreover, motivated by this correlation, we can devise a lookup table to pre-store the matrix representation with a smaller size than the one utilized in current implementations. In addition, this correlation also implies useful theoretical results which can be adopted to further demonstrate the advantages of binary matrix representation in vector LNC. In the following part of this paper, we focus on the study of vector LNC and investigate the applications of matrix representation related to the aspects of random and deterministic vector LNC.

Toward Memory-Efficient Analog Design Using Precomputed Lookup Tables

Analog design productivity remains a challenge in the digitally driven semiconductor chip design field. Knowledge-based and simulation-based analog automation approaches have not achieved widespread acceptance in the analog design community. Systematic analog design using precomputed lookup tables (LUTs) is a promising approach that can address the design productivity challenge. Although modern computing systems have powerful memory capabilities, which make the LUT approach viable, reducing the memory footprint of the LUTs remains a challenge. A memory-efficient design technique using LUTs is proposed by using an incomplete grid in the MOSFET degrees-of-freedom (DoFs) space. An efficient indexing technique for the incomplete grid is also proposed, using a precomputed offset array in various scenarios, such as two-sided constraints and three-dimensional LUTs. The results show that the proposed technique can achieve up to a 67% reduction in memory footprint, in addition to improving LUT generation time and query performance.

Estimation of electrical conductivity models using multi-coil rigid-boom electromagnetic induction measurements

Electromagnetic induction measurements from multi-coil configuration instruments are used to obtain information about the electrical conductivity distribution in the subsurface. The resulting inverse problem might not have a unique and stable solution. In that case, a local inversion method can be trapped in a local minimum and lead to an incorrect solution. In this study, we evaluate the well-posedness of the inverse problem for two and three-layered electrical conductivity models. We show that for a two-layered model, uniqueness is ensured only when both in-phase and quadrature data are available from the measurements. Results from a Gauss–Newton inversion and a lookup table demonstrate that the solution space is convex. Furthermore, we demonstrate that for even a simple three-layered model, the data contained in such measurements are insufficient to reach a correct or stable solution. For models with more than 2 layers, independent prior information is necessary to solve the inverse problem. The insights from the numerical examples are applied in a field case.

Low-latency 100 Gb/s PAM-4 PON with a 42.5 dB power budget over the 20 km anti-resonant hollow-core fiber.

The surging growth in data traffic has necessitated the development of higher-speed, lower-latency intensity modulation and direct detection (IM/DD) passive optical networks (PONs) with higher power budgets. To address the inherent limitations of traditional silica-based solid-core fibers, anti-resonant hollow-core fibers have garnered significant attention from both academia and industry. In this Letter, we present an experimental demonstration of 100 Gb/s PAM-4 IM/DD PON transmission over a 20 km anti-resonant hollow-core fiber in the C band, utilizing low-complexity digital signal processing (DSP). The result achieves a record high power budget of 42.5 dB, facilitated by a 3-tap weighted lookup table (LUT) at the optical line terminal (OLT) side and a semiconductor optical amplifier used as a preamplifier at the optical network unit (ONU) side. This represents the highest power budget reported for IM/DD PON to date, to the best of our knowledge,and offers a promising alternative for the future evolution of PON systems.

Compact widely tunable laser integrated on an indium phosphide membrane platform

We present the design, fabrication, and characterization results of a compact, widely tunable laser realized on an indium phosphide membrane-on-silicon (IMOS) platform. The laser features a compact Mach–Zehnder interferometric structure as the wavelength-selective intracavity filter with a footprint of 0.13 mm2. The filter design is optimized to ensure narrow filter transmission and high side-mode-to-main-mode-ratio, enabling single-mode operation for the laser. The high optical confinement on the IMOS platform can support tight waveguide bends. Leveraging this, the laser achieves a short cavity length, further enhancing the single-mode operation. Measurement results indicate a threshold current of 29 mA and a maximum on-chip output power of approximately 3.6 dBm and wall plug efficiency of 1.8%. The side-mode suppression ratio ranges from 30 to 44 dB, with a tuning range spanning 40 nm, from 1555 to 1595 nm. A complete tuning lookup table is generated via an automated setup incorporating a stochastic search algorithm.

3D Lightweight Cryptosystem Design For IoT Applications Based On Composite S-Box

The security of the Internet of Things (IoT) depends on strong cryptographic functions that require extensive computation and resources. The security of Internet of Things (IoT) relies on the use of strong cryptographic functions that demand extensive computation and resources. Therefore, the selection of a cryptographic system is influenced by the computational and communicational capabilities of IoT devices, including their energy requirements, memory constraints, and execution time. This paper aims to develop a lightweight and secure encryption algorithm for IoT applications, focusing on advancing cryptanalysis techniques. We propose a new lightweight algorithm named enhanced 3D RECTANGLE, designed to deliver robust security for IoT applications and optimized for cell phones with minimal memory usage, low power consumption, and efficient performance. The RECTANGLE block cipher was chosen for this research due to its high efficiency and speed relative to other lightweight algorithms, despite some security issues. The proposed enhanced 3D RECTANGLE algorithm improves confusion and diffusion properties through a new 3D array block rotation method for 4x4 plaintext, based on a 128-bit key and 16 rounds. Additionally, we designed a 4x4 composite S-Box with a Galois field pipelining structure, offering a more advanced solution compared to the Look-Up Table method. The cryptanalysis, avalanche effect, and bit error rate tests were conducted to verify the security strength of the proposed algorithm. The proposed algorithm was evaluated against two existing algorithms, RECTANGLE and Extended 3D RECTANGLE. The enhanced 3D RECTANGLE algorithm provides better cryptanalysis, demonstrating a 40% avalanche effect and achieving a bit error rate (BER) of 31%, indicating its greater effectiveness compared to the other two lightweight algorithms.

Demonstration of a low-complexity real-time FBMC transmitter for a spectral-efficiency IMDD system.

Filter bank multi-carriers (FBMC) have higher spectral efficiency, lower out-of-band spectrum leakage, and stronger ability to resist synchronization errors compared to orthogonal frequency-division multiplexing (OFDM), which has been widely considered as a promising solution for short-reach applications, such as passive optical networks. However, the traditional fast Fourier transform (FFT)-based FBMC scheme has quite high implementation complexity for high-speed optical communications. In this work, we proposed a low-complexity real-time FBMC transmitter scheme enabled by a single fully parallel pruned inverse FFT and simplified short prototype filter-based polyphase networks for a low-cost intensity-modulation and direct-detection (IMDD) system. Compared to the OFDM transmitter, the proposed FBMC one occupies a similar number of look-up tables and registers but saves 21% of real multipliers, reduces latency by 16.2%, and increases the data rate by 12.5%. The real-time FBMC/OFDM signals are generated with a field programmable gate array chip and 6-bit 30-GSa/s digital-to-analog converter and are experimentally demonstrated in an IMDD system. After a 20-km standard single-mode fiber transmission, the negligible power penalty can be achieved with the proposed FBMC transmitter scheme at the bit error rate of 3.8e-3 compared to the OFDM counterpart.

Systematical and universal calibration scheme for division-of-aperture polarimetric camera

Division-of-aperture polarimetric (DoAP) camera has been widely used to obtain polarization images of scene owing to its feature in simultaneous acquisition of polarization information, real-time measurement and compact system. However, practical applications of DoAP imaging inevitably involve calibrating the camera from several aspects, which is very essential but the related reports are relatively lacking in current research. In this paper, we proposed a systematical and universal calibration scheme for the DoAP camera, which includes the lens distortion correction, the image registration, the polarization channel calibration and the Stokes parameters modification. Accordingly, a software for calibration and imaging processing, which consists of four modules, is designed to collectively facilitate the efficient and accurate execution of all calibration steps. In order to achieve real-time polarimetric imaging, the lens distortion correction and the image registration were done by means of lookup tables. Experimental results by using a full-Stokes DoAP camera we fabricated indicate that, via the lens distortion correction and the image registration, the alignment error between two images with a mean absolute error (MAE) less than 0.16 pixels can be obtained, resulting in the aligned images having an average increase of 64.53 % for the structural similarity index (SSIM) and that of 84.97 % for the peak signal to noise ratio (PSNR), respectively; and further via the polarization channel calibration and the Stokes parameters modification, the MAE of 0.0146 for the degree of polarization (DoP) and that of 0.3544° for the angle of polarization (AoP) are obtained, respectively. This proposed calibration scheme for DoAP camera is systematic, automatic and robust, which is benefit of improving the polarization detection accuracy, enhancing the camera calibration efficiency and thus promoting its polarimetric imaging performance.

Integrating error correction and detection techniques in RISC-V processor microarchitecture for enhanced reliability

An essential consideration in processor design is ensuring reliability, particularly in demanding environments such as outer space and nuclear plants. To mitigate the effects of errors and enable error recovery, processors need to incorporate fault tolerance techniques. One common type of error is SEU (Single Event Upset), which affects various microelectronic devices including microprocessors, microcontrollers, and semiconductor memory devices. While error mitigation techniques have been developed for processors based on architectures like ARM (Advanced RISC Machine) and MIPS (Million Instructions Per Second), there is a gap in research for open-source ISAs (Instruction Set Architecture) like RISC-V, which this paper aims to address. This paper focuses on designing a fault-tolerant microarchitecture for a RISC-V processor that can correct one-bit errors, detect up to two-bit errors, and integrate lockstep and pipeline rollback features at a lower LUTs (Look Up Tables) consumption by re-using the same hardware pipeline for error mitigation and recovery through instruction mimicking. By incorporating these features, the proposed approach enhances the system’s fault tolerance by detecting and correcting errors caused by transient events and achieves a lower effective die size upon realization compared to contemporary works. The proposed microarchitecture design was simulated and synthesized using the Vivado Design Suite 2023.1 and implemented on a Zynq 7000 SoC ZC702 Evaluation Kit.

Comparative Study on the Vertical Column Concentration Inversion Algorithm of Tropospheric Trace Gas Based on the MAX-DOAS Measurement Spectrum

The tropospheric vertical column concentration (VCDtrop) of NO2, SO2, and HCHO was retrieved, respectively, by employing the geometric method (Geomtry), simplified model method (Model), and look-up table method (Table) with the observation spectra of the multi-axis differential absorption spectroscopy instrument (MAX-DOAS). The correlation and relative differences of the inversion results obtained by these three algorithms, as well as the changes in quantiles, were explored. The comparative analysis reveals that the more concentrated the vertical distribution height of gas components is in the near-surface layer, the better the conformity of the VCDtrop retrieved by different algorithms. However, the increase in relative differences is also related to the diurnal variation of gas components. The influence of aerosols on the inversion of the VCDtrop is greater than the change in the vertical distribution height of the gas component itself. The near-surface concentration and distribution height of gas components are the internal factors that give rise to relative differences in the inversion of the VCDtrop by different algorithms, while aerosols are one of the extremely important external reasons. The VCDtrop inverted by Geomtry without considering the influence of aerosols is generally larger except for NO2. Model sets up aerosols in accordance with the height and meteorological conditions of the atmospheric environment. Table can invert the aerosol profile in real time. Compared with Model, it shows a significant improvement in the refined setting of aerosols. Moreover, while obtaining the vertical distribution of aerosols, it can invert the diurnal variation of the VCDtrop. The VCDtrop inverted by Table is the smallest, and the relative difference with Model is on average about 10% smaller. The relative difference of the VCDtrop for the same height (aerosol optical thickness) quantile is 7–15% (about 25% lower on average). When comparing the inversion results of Table with the Ozone Monitoring Instrument (OMI) satellite product, the MAX-DOAS inversion results of NO2, SO2, and HCHO are all larger than the OMI product. This is related to the different observation methods of the MAX-DOAS and OMI and the configuration between the aerosol layer and the distribution height of gas components.

A taxonomy of automatic differentiation pitfalls

AbstractAutomatic differentiation is a popular technique for computing derivatives of computer programs. While automatic differentiation has been successfully used in countless engineering, science, and machine learning applications, it can sometimes nevertheless produce surprising results. In this paper, we categorize problematic usages of automatic differentiation, and illustrate each category with examples such as chaos, time‐averages, discretizations, fixed‐point loops, lookup tables, linear solvers, and probabilistic programs, in the hope that readers may more easily avoid or detect such pitfalls. We also review debugging techniques and their effectiveness in these situations.This article is categorized under: Technologies > Machine Learning

Integration of a parameter combination discriminator improves the accuracy of chlorophyll inversion from spectral imaging of rice

The PROSPECT model, widely employed for leaf radiation transfer analysis, relies heavily on input biochemical parameters to calculate spectral reflectance. This dependence often results in similar simulated spectra for different parameter combinations, which complicates the inversion of leaf chlorophyll content (Cab). To address this ill-posed problem, we enhanced the model's application by integrating a support vector machine (SVM)-based parameter combination discriminator with the Look-Up Table (LUT) constructed from the PROSPECT model. We marked samples in the LUT to reflect their closeness to measured parameters, facilitating the identification of reasonable versus unreasonable parameter combinations. The discriminator could effectively discriminate between reasonable and unreasonable parameter combinations, achieving accuracies of 0.894 and 0.888 in the training and test sets, respectively. The discriminator was then employed to refine the LUT, and an improved third-generation non-dominated ranking genetic algorithm (NSGA-III) was used to optimize the extreme learning machine. The inversion of rice Cab using the refined LUT and the NSGA-III demonstrated substantial improvements. The LUT was significantly improved after integration with the discriminator, yielding R2 and RMSE of 0.665 and 7.220 ​μg ​cm−2, respectively. The NSGA-III inversion, which utilized the “constraint method” with discriminator results as optimization objectives, achieved the best inversion accuracy, with R2 and RMSE values of 0.809 and 4.788, respectively. This study demonstrates that the effective use of a parameter discriminator can significantly enhance the accuracy of Cab inversion based on the PROSPECT model, offering a substantial advancement in addressing its inherent ill-posed challenges.

A Calibration Scheme for a 800-Ms/s 14 Bit Pipelined ADC in 28-nm CMOS

Abstract A calibration scheme is proposed to compensate linear and nonlinear errors including offset, cap mismatch, second- and higher-order harmonics and memory induced errors in pipelined analog-to-digital converters (ADCs). The alternating minimization (AM) algorithm is proposed to accurately estimate the coefficients of the errors. As an accurate input value is hardly acquired in practical measurements, instead, the proposed scheme estimates the input and the error coefficients jointly. Once the error coefficient was calculated, it will be recorded in a lookup table (LUT). The scheme has been demonstrated in a 800-Ms/s 14-bit pipelined ADC fabricated in a 28nm CMOS process, which features a signal-to-noise ratio (SNR) and spurious-free dynamic range (SFDR) improvement of 2.49 dB and 11.49 dB at 800-Ms/s.

An FPGA-Implemented Antinoise Fuzzy Recurrent Neural Network for Motion Planning of Redundant Robot Manipulators.

When a robot completes end-effector tasks, internal error noises always exist. To resist internal error noises of robots, a novel fuzzy recurrent neural network (FRNN) is proposed, designed, and implemented on field-programmable gated array (FPGA). The implementation is pipeline-based, which guarantees the order of overall operations. The data processing is based on across-clock domain, which is beneficial for computing units' acceleration. Compared with traditional gradient-based neural networks (NNs) and zeroing neural networks (ZNNs), the proposed FRNN has faster convergence rate and higher correctness. Practical experiments on a 3 degree-of-freedom (DOs) planar robot manipulator show that the proposed fuzzy RNN coprocessor needs 496 lookup table random access memories (LUTRAMs), 205.5 block random access memories (BRAMs), 41384 lookup tables (LUTs), and 16743 flip-flops (FFs) of the Xilinx XCZU9EG chip.

Intelligent Radiometric Calibration System for Ocean Color Satellite Sensors

Abstract To achieve the integration of multiple ocean color (OC) sensors’ radiometric calibration tasks into a single system, we have developed an intelligent on-orbit radiometric calibration system called the Generalized Radiometric Calibration Entity for Ocean Color (Grace-OC). The system features real-time data downloading capabilities and integrates three calibration methods: onboard calibration, system vicarious calibration and cross calibration, enabling intelligent selection of on-orbit radiometric calibration methods tailored to the calibration sensors. Compared to other calibration systems, we have improved the universality and efficiency of the system by establishing high-spectral aerosols and Rayleigh lookup tables (LUTs) which are verified consistency through a comparative analysis with operational LUTs releasing by National Aeronautics and Space Administration (NASA) in this paper. Building upon this foundation, we have integrated a comprehensive analysis function for calibration coefficients to automatically construct degradation models of the radiometric measurement performance, and to achieve mutual verification between different calibration methods. We applied Grace-OC to HY1C/D and verified the feasibility of the intelligent selection calibration methods and the stability of the calibration system, achieving a calibration accuracy of up to 0.5%. Simultaneously, the precision of degradation models of the radiometric measurement performance is confirmed through Grace-OC, and the on-orbit radiometric calibration task was ultimately completed within 6 minutes for each per scene. Based on the above applications, Grace-OC has demonstrated its universality for various OC sensors, as well as the stability of on-orbit radiometric calibration tasks and the efficiency of operational speed.