Look-up Table Research Articles

Dynamic wind farm flow control using free-vortex wake models

Abstract. A novel dynamic economic model-predictive control strategy is presented that improves wind farm power production and reduces the additional demands of wake steering on yaw actuation when compared to an industry state-of-the-art reference controller. The novel controller takes a distributed approach to yaw control optimisation using a free-vortex wake model. An actuator-disc representation of the wind turbine is employed and adapted to the wind farm scale by modelling secondary effects of wake steering and connecting individual turbines through a directed graph network. The economic model-predictive control problem is solved on a receding horizon using gradient-based optimisation, demonstrating sufficient performance for realising real-time control. The novel controller is tested in a large-eddy simulation environment and compared against a state-of-the-art look-up table approach based on steady-state model optimisation and an extension with wind direction preview. Under realistic variations in wind direction and wind speed, the preview-enabled look-up table controller yielded the largest gains in power production. The novel controller based on the free-vortex wake produced smaller gains in these conditions while yielding more power under large changes in wind direction. Additionally, the novel controller demonstrated potential for a substantial reduction in yaw actuator usage.

A guide to residential energy storage and rooftop solar: State net metering policies and utility rate tariff structures

Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by discussing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies and above-average electric utility rates.

Efficient Look-Up Table from Expanded Convolutional Network for Accelerating Image Super-resolution

The look-up table (LUT) has recently shown its practicability and effectiveness in super-resolution (SR) tasks due to its low computational cost and hardware independence. However, most existing methods focus on improving the performance of SR, neglecting the demand for high-speed SR on low-computational edge devices. In this paper, we propose an efficient expanded convolution (EC) layer, which expands the output size of regular convolution to enlarge the receptive field (RF) indirectly. It can increase the size of the LUT corresponding to the network linearly with the increase of RF. Additionally, after introducing the EC, multiple LUTs are merged into one LUT, achieving faster running speed while maintaining SR performance. More specifically, we expand the coverage of the convolutional output so that the output at the current position covers the target position and its surroundings, forming an overlapping sliding window at the output end. We sum up the overlapping parts of the sliding window as the output, thereby achieving the effect of enlarging the RF size. Moreover, by expanding the numerical range of the accumulated results and rescaling them to [0,255], the method can mitigate the error caused by quantization output. Experiments indicate that the proposed method performs better than the baseline method and is faster than other LUT-based SR methods.

BVT-IMA: Binary Vision Transformer with Information-Modified Attention

As a compression method that can significantly reduce the cost of calculations and memories, model binarization has been extensively studied in convolutional neural networks. However, the recently popular vision transformer models pose new challenges to such a technique, in which the binarized models suffer from serious performance drops. In this paper, an attention shifting is observed in the binary multi-head self-attention module, which can influence the information fusion between tokens and thus hurts the model performance. From the perspective of information theory, we find a correlation between attention scores and the information quantity, further indicating that a reason for such a phenomenon may be the loss of the information quantity induced by constant moduli of binarized tokens. Finally, we reveal the information quantity hidden in the attention maps of binary vision transformers and propose a simple approach to modify the attention values with look-up information tables so that improve the model performance. Extensive experiments on CIFAR-100/TinyImageNet/ImageNet-1k demonstrate the effectiveness of the proposed information-modified attention on binary vision transformers.

Cosine convolutional neural network and its application for seizure detection

Traditional convolutional neural networks (CNNs) often suffer from high memory consumption and redundancy in their kernel representations, leading to overfitting problems and limiting their application in real-time, low-power scenarios such as seizure detection systems. In this work, a novel cosine convolutional neural network (CosCNN), which replaces traditional kernels with the robust cosine kernel modulated by only two learnable factors, is presented, and its effectiveness is validated on the tasks of seizure detection. Meanwhile, based on the cosine lookup table and KL-divergence, an effective post-training quantization algorithm is proposed for CosCNN hardware implementation. With quantization, CosCNN can achieve a nearly 75% reduction in the memory cost with almost no accuracy loss. Moreover, we design a configurable cosine convolution accelerator on Field Programmable Gate Array (FPGA) and deploy the quantized CosCNN on Zedboard, proving the proposed seizure detection system can operate in real-time and low-power scenarios. Extensive experiments and comparisons were conducted using two publicly available epileptic EEG databases, the Bonn database and the CHB-MIT database. The results highlight the performance superiority of the CosCNN over traditional CNNs as well as other seizure detection methods.

NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement

3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Implicit LUT (NILUT), an implicitly defined continuous 3D color transformation parameterized by a neural network. We show that NILUTs are capable of accurately emulating real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly. Our novel approach is memory-efficient, controllable and can complement previous methods, including learned ISPs. Code at https://github.com/mv-lab/nilut

PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling

Pre-routing timing prediction has been recently studied for evaluating the quality of a candidate cell placement in chip design. It involves directly estimating the timing metrics for both pin-level (slack, slew) and edge-level (net delay, cell delay), without time-consuming routing. However, it often suffers from signal decay and error accumulation due to the long timing paths in large-scale industrial circuits. To address these challenges, we propose a two-stage approach. First, we propose global circuit training to pre-train a graph auto-encoder that learns the global graph embedding from circuit netlist. Second, we use a novel node updating scheme for message passing on GCN, following the topological sorting sequence of the learned graph embedding and circuit graph. This scheme residually models the local time delay between two adjacent pins in the updating sequence, and extracts the lookup table information inside each cell via a new attention mechanism. To handle large-scale circuits efficiently, we introduce an order preserving partition scheme that reduces memory consumption while maintaining the topological dependencies. Experiments on 21 real world circuits achieve a new SOTA R2 of 0.93 for slack prediction, which is significantly surpasses 0.59 by previous SOTA method. Code will be available at: https://github.com/Thinklab-SJTU/EDA-AI.

Signatures of microstructure in gradient-echo andspin-echo signals.

To determine whether the spatial scale and magnetic susceptibility of microstructure can be evaluated robustly from the decay of gradient-echo and spin-echo signals. Gradient-echo and spin-echo images were acquired from suspensions of spherical polystyrene microbeads of 10, 20, and 40 μm nominal diameter. The sizes of the beads and their magnetic susceptibility relative to the medium were estimated from the signal decay curves, using a lookup table generated from Monte Carlo simulations and an analytic model based on the Gaussian phase approximation. Fitting Monte Carlo predictions to spin-echo data yielded acceptable estimates of microstructural parameters for the 20 and 40 μm microbeads. Using gradient-echo data, the Monte Carlo lookup table provided satisfactory parameter estimates for the 20 μm beads but unstable results for the diameter of the largest beads. Neither spin-echo nor gradient-echo data allowed accurate parameter estimation for the smallest beads. The analytic model performed poorly over all bead sizes. Microstructural sources of magnetic susceptibility produce distinctive non-exponential signatures in the decay of gradient-echo and spin-echo signals. However, inverting the problem to extract microstructural parameters from the signals is nontrivial and, in certain regimes, ill-conditioned. For microstructure with small characteristic length scales, parameter estimation is hampered by the difficulty of acquiring accurate data at very short echo times. For microstructure with large characteristic lengths, the gradient-echo signal approaches the static-dephasing regime, where it becomes insensitive to size. Applicability of the analytic model was further limited by failure of the Gaussian phase approximation for all but the smallest beads.

Basic Approaches for Reducing Power Consumption in Finite State Machine Circuits—A Review

Methods for reducing power consumption in circuits of finite state machines (FSMs) are discussed in this review. The review outlines the main approaches to solving this problem that have been developed over the last 40 years. The main sources of power dissipation in CMOS circuits are shown; the static and dynamic components of this phenomenon are analyzed. The power consumption saving can be achieved by using coarse-grained methods common to all digital systems. These methods are based on voltage or/and clock frequency scaling. The review shows the main structural diagrams generated by the use of these methods when optimizing the power characteristics of FSM circuits. Also, there are various known fine-grained methods taking into account the specifics of both FSMs and logic elements used. Three groups of the fine-grained methods targeting FPGA-based FSM circuits are analyzed. These groups include clock gating, state assignment, and replacing look-up table (LUT) elements by embedded memory blocks (EMBs). The clock gating involves a separate or joint use of such approaches as the (1) decomposition of FSM inputs and (2) disabling FSM inputs. The aim of the power-saving state assignment is to reduce the switching activity of a resulting FSM circuit. The replacement of LUTs by EMBs allows a reduction in the power consumption due to a decrease in the number of FSM circuit elements and their interconnections. We hope that the review will help experts to use known methods and develop new ones for reducing power consumption. We think that a good knowledge and understanding of existing methods of reducing power consumption is a prerequisite for the development of new, more effective methods to solve this very important problem. Although the methods considered are mainly aimed at FPGA-based FSMs, they can be modified, if necessary, and used for the power consumption optimization of FSM circuits implemented with other logic elements.

Rotor Performance Predictions for Urban Air Mobility: Single vs. Coaxial Rigid Rotors

This work details the development and validation of a methodology for high-resolution rotor models used in hybrid Blade Element Momentum Theory Unsteady Reynolds Averaged Navier–Stokes (BEMT-URANS) CFD. The methodology is shown to accurately predict single and coaxial rotor performance in a fraction of the time required by conventional CFD methods. The methodology has three key features: (1) a high-resolution BEMT rotor model enabling large reductions in grid size, (2) a discretized set of momentum sources to interface between the BEMT rotor model and the structured URANS flow solver, and (3) leveraging of the first two features to enable highly parallelized GPU-accelerated multirotor CFD simulations. The hybrid approach retains high-fidelity rotor inflow, wake propagation, and rotor–rotor interactional effects at a several orders of magnitude lower computational cost compared to conventional blade-resolved CFD while retaining high accuracy on steady rotor performance metrics. Rotor performance predictions of thrust and torque for both single and coaxial rotor configurations are compared to test the data that the authors obtained at the NASA Langley 14- by 22-ft. Subsonic Tunnel Facility. Simulations were run with both fully turbulent and free-transition airfoil performance tables to quantify the associated uncertainty. Single rotor thrust and torque were predicted on average within 4%. Coaxial thrust and power were predicted within an average of 5%. A vortex ring state (VRS) shielding phenomenon for coaxial rotor systems is also presented and discussed. The results support that this hybrid BEMT-URANS CFD methodology can be highly parallelized on GPU machines to obtain accurate rotor performance predictions across the full spectrum of possible UAM flight conditions in a fraction of the time required by conventional higher-fidelity methods. This strategy can be used to rapidly create look-up tables with hundreds to thousands of flight conditions using a three-dimensional multirotor CFD for UAM.

Optimized LNS-FAID of LDPC codes: a hybrid precision decoding approach for 50G-PON.

Efficient error correction in high-speed communication networks, such as the 50G passive optical network (50G-PON), is paramount. This Letter focuses on optimizing a layered non-surjective finite alphabet iterative decoder (LNS-FAID) for 50G-PON, with an emphasis on high-throughput and low-power consumption. We propose using a distinct lookup table (LUT) for each iteration to enhance decoding performance and lower error floors. Additionally, we improve the 2-bit LNS-FAID architecture by adding operational states and a sign backtracking (SBT) strategy. This paper also introduces a hybrid precision model that merges 3-bit and 2-bit LNS-FAIDs, which balances error correction with computational efficiency. Our simulation results show that these approaches significantly improve the performance of the LDPC code in 50G-PON.

A real time hardware design for bilateral filtering

Bilateral filtering is a widely used video image filtering method, but its computational complexity is high, which is not conducive to hardware real-time applications. Therefore, this paper presents a hardware structure of bilateral filtering for real-time applications of high-definition images. Our hardware design mainly includes lookup table structure and parallel structure designs, respectively. Based on the Vivado HLS evaluation, the experimental results show that the hardware structure of this paper can meet the real-time application of bilateral filtering in high-definition video images.

Dependence of critical heat flux in vertical flow systems on dimensional and dimensionless parameters using machine learning

The critical heat flux (CHF) associated with the departure from nucleate boiling (DNB) determines the design and safety aspects of two-phase flow boiling systems. Despite the availability of several predictive tools, within the thermal engineering community, the pursuit of an accurate and robust CHF model remains a significant challenge. In this unique study, we extracted a substantial database from literature to develop machine-learning (ML) models to predict CHF for vertical flows commonly employed in the process industry. The extensive database encompasses 15,006 experimental data points gathered from diverse sources covering wide range of geometric and operational ranges of D, L, P, G, h, and x. This database is then employed to develop five ML – based models, namely Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Random Forests (RF). The selection of optimal input features is conducted by exploring various combinations of both dimensional and dimensionless parameters to identify the most influencing inputs for CHF prediction. The models incorporating features D, L, P, G, h, x, L/D, ρr, We, and BO achieve CHF predictions with Mean Absolute Errors (MAEs) of 8.85 % for the ANN model and 10.39 % for the XGBoost model. The optimized ML models outperformed even the highly reliable CHF correlations and lookup tables. The feature importance analysis of input parameters highlights strong dependence of CHF on dimensionless numbers such as Weber number and Boiling number.

Lookup Table-Based Design of Scalar Multiplication for Elliptic Curve Cryptography

This paper is aimed at using a lookup table method to improve the scalar multiplication performance of elliptic curve cryptography. The lookup table must be divided into two polynomials and requires two iterations of point doubling operation, for which negation operations are needed. It is well known that an inversion operation requires a lot of multiplication time. The advantage of this paper is that we are able to reduce one inverse element calculation for this problem and also improve the basic operations of finite fields through segmentation methods. If the normal basis method is used in the design of the inverse element operation, it must be converted to the normal basis through the standard basis. However, the conversion process requires a lot of matrix operations. Though the anti-element operation has good speed performance, it also increases the computational complexity. Using number theory and grouping methods will greatly improve the performance of inverse element operations. With application of the two-time point doubling operation in the hardware implementation, the developed approach reduces the computing time by 48% as compared with the conventional approach. The computational time of the scalar multiplication using the presented method is further improved by 67% over the traditional algorithm with only an area increase of 12%. Finally, the proposed lookup table-based technique can be utilized for software and hardware implementation, as the developed arithmetic operations are simple and are consistent in their execution.

Random forest evaluation using multi-key homomorphic encryption and lookup tables

In recent years, machine learning (ML) has become increasingly popular in various fields of activity. Cloud platforms have also grown in popularity, as they offer services that are more secure and accessible worldwide. In this context, cloud-based technologies emerged to support ML, giving rise to the machine learning as a service (MLaaS) concept. However, the clients accessing ML services in order to obtain classification results on private data may be reluctant to upload sensitive information to cloud. The model owners may also prefer not to outsource their models in order to prevent model inversion attacks and to protect intellectual property. The privacy-preserving evaluation of ML models is possible through multi-key homomorphic encryption (MKHE), that allows both the client data and the model to be encrypted under different keys. In this paper, we propose an MKHE evaluation method for decision trees and we extend the proposed method for random forests. Each decision tree is evaluated as a single lookup table, and voting is performed at the level of groups of decision trees in the random forest. We provide both theoretical and experimental evaluations for the proposed method. The aim is to minimize the performance degradation introduced by the encrypted model compared to a plaintext model while also obtaining practical classification times. In our experiments with the proposed MKHE random forest evaluation method, we obtained minimal (less than 0.6%) impact on the main ML performance metrics considered for each scenario, while also achieving reasonable classification times (of the order of seconds).

Five-phase space vector carrier-based PWM for third harmonic injection

Five-phase AC drives allow a third harmonic voltage to be fed into the stator winding – in addition to the fundamental voltage – to generate a third harmonic magnetic air gap field. This allows the generation of an additional small constant torque. Furthermore, the undesirable saturation harmonics that occur with saturated iron can be compensated. The desired third harmonic voltage is commonly generated via time-consuming space vector PWM algorithms, which are not well-suited for real-time application, or via look-up tables, which require an increased storage capacity. In our case, the desired third harmonic voltage is generated in the five-phase voltages by adding a non-sinusoidal zero-sequence voltage with five times the fundamental frequency to the fundamental voltage reference signals in carrier-based pulse width modulation (PWM) with a constant switching frequency. This is equivalent to a space vector PWM method (SVPWM) and avoids a significantly high calculation effort. The PWM algorithm programmed in MATLAB in real time allows the fast generation of a third harmonic voltage per phase with freely definable amplitude and phase shift without requiring a lot of storage capacity.

Impeccable Keccak

The standardization of the hash-based digital signature scheme SPHINCS+ proceeds faster than initially expected. This development seems to be welcomed by practitioners who appreciate the high confidence in SPHINCS+’s security assumptions and its reliance on well-known hash functions. However, the implementation security of SPHINCS+ leaves many questions unanswered, due to its proneness to fault injection attacks. Previous works have shown, that even imprecise fault injections on the signature generation are sufficient for universal forgery. This led the SPHINCS+ team to promote the usage of hardware countermeasures against such attacks. Since the majority of operations in SPHINCS+ is dedicated to the computation of the Keccak function, we focus on its security. At the core, hardware countermeasures against fault injection attacks are almost exclusively based on redundancy. For hash functions such as Keccak, straightforward instance- or time-redundancy is expensive in terms of chip area or latency. Further, for applications that must withstand powerful fault adversaries, these simple forms of redundancy are not sufficient. To this end, we propose our impeccable Keccak design. It is based on the methodology presented in the original Impeccable Circuits paper by Aghaie et al. from 2018. On the way, we show potential pitfalls when designing impeccable circuits and how the concept of active security can be applied to impeccable circuits. To the best of our knowledge, we are the first to provide proofs of active security for impeccable circuits. Further, we show a novel way to implement non-linear functions without look-up tables. We use our findings to design an impeccable Keccak. Assuming an adversary with the ability to flip single bits, our design detects all attacks with three and less flipped bits. Attacks from adversaries who are able to flip four or more bits are still detected with a high probability. Thus, our design is one of the most resilient designs published so far and the only Keccak design that is provably secure within a bit-flip model. At an area overhead of factor 3.2, our design is competitive with state-of-the-art designs with less resilience.

Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging.

The conventional optical properties (OPs) reconstruction in spatial frequency domain (SFD) imaging, like the lookup table (LUT) method, causes OPs aliasing and yields only average OPs without depth resolution. Integrating SFD imaging with time-resolved (TR) measurements enhances space-TR information, enabling improved reconstruction of absorption () and reduced scattering () coefficients at various depths. To achieve the stratified reconstruction of OPs and the separation between and , using deep learning workflow based on the temporal and spatial information provided by time-domain SFD imaging technique, while enhancing the reconstruction accuracy. Two data processing methods are employed for the OPs reconstruction with TR-SFD imaging, one is full TR data, and the other is the featured data extracted from the full TR data (, continuous-wave component, , mean time of flight). We compared their performance using a series of simulation and phantom validations. Compared to the LUT approach, utilizing full TR, and datasets yield high-resolution OPs reconstruction results. Among the three datasets employed, full TR demonstrates the optimal accuracy. Utilizing the data obtained from SFD and TR measurement techniques allows for achieving high-resolution separation reconstruction of and at different depths within 5mm.

Efficient implementation of complex equations of state in a high-order framework

In the last decades, the interest in non-ideal compressible fluid dynamics (NICFD) flows and high-order accurate numerical methods, such as discontinuous Galerkin (dG), has quickly grown. In fact, advanced simulation capabilities are of paramount importance to develop new sustainable technologies with higher efficiency and low environmental impact, and to decrease the use of expensive experimental facilities. Nowadays however, the coupling of accurate Computational Fluid Dynamics (CFD) solvers with sophisticated thermodynamic models has been investigated mainly in the finite volume (FV) framework. Moreover, the solution of complex equations of state (EoS) has a too high computational cost for real-life use in industry, which is often overcome with the look-up table (LuT) interpolation approach. LuTs seem to be more suitable for FV solvers rather than high-order or finite element ones, since the interpolation error introduced may deteriorate the higher accuracy provided by these numerical methods. The novelty of this work lies in the assessment of an efficient implementation for real gas simulations in a high-order solver, by resorting to structured LuTs and automatic differentiation (AD). The proposed implementation is assessed with the computation of the inviscid and turbulent flow around a NACA0012. This approach guarantees an overhead of the computational cost around 17% with respect to an ideal gas solver with manually derived jacobian matrix.

Angiographic measurement of the superficial temporal artery for potential use in cerebral bypass surgery: a combined radiological and cadaveric study.

This study aims to investigate the microsurgical anatomy of the superficial temporal artery (STA), explore the relationship between STA length and lumen diameter, and develop a reliable radiologic method for selecting STA segments for bypass surgery. This study used 10 cadaveric dissections (20 STAs, both sides) and 20 retrospective radiological examinations (40 STAs, both sides), employing curved multiplanar reformation and flow color lookup table (CLUT) DICOM processing. Measurements included vessel lumen diameters and luminal cross-sectional thicknesses 3mm proximal to the STA bifurcation, 3mm distal to the frontal branch, 5cm distal to the frontal branch, 3mm distal to the parietal branch, and 5cm distal to the parietal branch. The distance between the STA bifurcation and the superior zygomatic border (SZB) was also measured. In our analysis, descriptive statistics encompassed mean, standard deviation (SD), standard error, minimum and maximum values, and distributions. Comparative statistics were performed using Student's t-test, with statistical significance set at p < 0.05. There were no statistically significant differences between STA measurements of bifurcation distances (p = 0.88) and lumen diameters (p = 0.46) between cadavers and radiological measures. However, lumen thicknesses were larger in frontal branches than parietal branches at the seventh and eighth centimeter (p = 0.012, p = 0.039). Branches became thinner distally from the zygoma in both cadavers and radiological image measurements. The CLUT DICOM processing radiological measures provided the high-precision required to enable pre-surgical vessel selection for extracranial-intracranial bypass. The results show that STA vessel luminal diameters are sufficient (> 1mm) for bypass surgery in the first 9cm but gradually decrease after that. Also shown is that the choice of frontal versus parietal branches depends on individual anatomical features; therefore, careful preoperative radiological examination is critical.