Reconfigurable Array Research Articles

Dynamic parallel imaging at 9.4 T using reconfigurable receive coaxial dipoles.

Parallel imaging is one of the key MRI technologies that allow reduction of image acquisition time. However, the parallel imaging reconstruction commonly leads to a signal-to-noise ratio (SNR) drop evaluated using a so-called geometrical factor (g-factor). The g-factor is minimized by increasing the number of array elements and their spatial diversity. At the same time, increasing the element count requires a decrease in their size. This may lead to insufficient coil loading, an increase in the relative noise contribution from the RF coil itself, and hence SNR reduction. Previously, instead of increasing the channel number, we introduced the concept of electronically switchable time-varying sensitivities, which was shown to improve parallel imaging performance. In this approach, each reconfigurable receive element supports two spatially distinct sensitivity profiles. In this work, we developed and evaluated a novel eight-element human head receive-only reconfigurable coaxial dipole array for human head imaging at 9.4 T. In contrast to the previously reported reconfigurable dipole array, the new design does not include direct current (DC) control wires connected directly to the dipoles. The coaxial cable itself is used to deliver DC voltage to the PIN diodes located at the ends of the antennas. Thus, the novel reconfigurable coaxial dipole design opens a way to scale the dynamic parallel imaging up to a realistic number of channels, that is, 32 and above. The novel array was optimized and tested experimentally, including in vivo studies. It was found that dynamic sensitivity switching provided an 8% lower mean and 33% lower maximum g-factor (for Ry × Rz = 2 × 2 acceleration) compared with conventional static sensitivities.

Frequency reconfigurable antenna array modelling based on MoM-GEC method for RFID, WiMax and WLAN applications

Frequency reconfigurable antenna array modelling based on MoM-GEC method for RFID, WiMax and WLAN applications

A Conv‐GEMM reconfigurable accelerator with WS‐RS dataflow for high throughput processing

AbstractConvolution and matrix operations are both important computations in Deep Neural Networks (DNNs). However, the significant differences between convolution and matrix computation patterns have posed a challenge in efficiently supporting both convolution (Conv) and general matrix multiplication (GEMM) on hardware design. This paper proposes a Conv‐GEMM reconfigurable accelerator architecture for high throughput edge processing. A weight stationary‐row streaming (WS‐RS) dataflow scheme is proposed, which maximizes data reuse through hierarchical memory structures and flexible PE connections, and supports high throughput edge‐based deep learning algorithms. Based on the proposed dataflow, multi‐scale memory access network (MMAN), reconfigurable accumulator array (RAA), and configurable instruction set architecture (ISA) are designed to optimize computation throughput and energy efficiency. The accelerator is designed under 65 nm technology, achieves peak performance of 1.15 TOPS at 250 MHz, with an energy efficiency of 1.14 TOPS/W. The GEMM computation achieves 85.7% latency improvement and the Mobilenet‐V1 processing achieves a throughput of 529 fps under a 256 × 224 image size and an 87.15% (top‐5) accuracy on the ImageNet dataset.

Reconfigurable coherent nanolaser array based on twisted systems

Reconfigurable coherent nanolaser array based on twisted systems

Increasing The Efficiency Of The Photovoltaic (PV) Array Utilization With Experimental Results

The reason for this paper is to give methods verified experimentally, to use in an efficient manner, a green system using photovoltaic energy. This study concerns optimization of photovoltaic system operation, in real time. Tracking maximum power method has been verified experimentally. Another technique for optimum switching of photovoltaic (PV) modules with electrical array reconfiguration is proposed, whatever are conditions (insulation, temperature, load). A microcontroller is used as support of the electronic control and practical tests are presented.

Enhancement of photovoltaic array characteristics and global maximum power using Padovan transform‐based image encryption strategy

SummaryDuring shading, the mismatch between the panels in the photovoltaic (PV) array mitigates the global maximum power (GMP). Besides, the mismatch in the irradiation levels of distinct rows of the PV array instigates multiple power peaks (MPPs) in the array characteristics. Distinguishing the local and global peaks among MPPs for tracking the GMP is highly challenging for maximum power point tracking (MPPT) controllers. So, to mitigate the MPPs and enhance the GMP, array reconfiguration is preferred. Nevertheless, most existing reconfiguration techniques exhibit poor shade dispersal, distorted electrical characteristics, multiple MPPs, increased mismatch, scalability issues, etc. To overcome these challenges, this paper proposes a new Padovan transform‐based encryption strategy for array reconfiguration. The proposed method was evaluated for both symmetric and unsymmetrically sized arrays. Its performance has also been compared to that of 23 other strategies. The proposed reconfiguration strategy integrated with MPPT is validated experimentally using a prototype model. A nonparametric statistical hypothesis test with a p‐value of 0.05 has been used for a pairwise fair comparison study among the examined approaches. The proposed approach constantly outperforms the current methods because of its unique shade dispersion generated through intelligent reconfiguration offering the GMP improvement of 34.429%, 12.51%, 5.05%, and 37.40%, 22.93%, 16.51% for 9 × 9 and 4 × 8 PV arrays, respectively.

A Reconfigurable SRAM CRP PUF with High Reliability and Randomness

This paper presents a novel reconfigurable SRAM CRP PUF that can achieve high reliability and randomness. In conventional reconfigurable SRAM CRP PUFs, imprecise timing control can produce a biased response output, which is typically attributed to mismatches in the connection of input control signals to the two inverter arrays in the layout floorplan. We propose a timing control scheme along with the addition of an adjunct NMOS transistor to address this issue. This eliminates the connection mismatches for the challenge and word-line inputs to the two inverter arrays. Furthermore, we employ symmetric layout techniques to achieve the randomness of response output. The symmetric arrangement of the two inverter arrays maximizes the inherent random output characteristics derived from process variation. The pre-charge input signal is symmetrically connected to each array to prevent delay mismatches. A 16 × 9-bit reconfigurable PUF array is fabricated by using a 180 nm CMOS process, with a PUF cell area of 1.2 × 104 F2/bit. The experimental results demonstrate an inter Hamming distance of 0.4949 across 40 chips and an intra Hamming distance of 0.0167 for a single chip in 5000 trials. The measured worst bit error rate (BER) is 4.86% at the nominal point (1.8 V, 25 °C). The proposed prototype exhibits good reliability and randomness, as well as a small silicon area when compared to the conventional SRAM CRP PUFs.

Dynamic reconfiguration of photovoltaic arrays for laser wireless power transmission via Deep neural networks

Dynamic reconfiguration of photovoltaic arrays for laser wireless power transmission via Deep neural networks

Additively-Manufactured, Magnetically-Controlled, Frequency and Polarization Reconfigurable Phased Array Antenna

Additively-Manufactured, Magnetically-Controlled, Frequency and Polarization Reconfigurable Phased Array Antenna

Single-Motor Controlled Mechanically Frequency Reconfigurable Unidirectional Antenna Array with Stable Radiation Patterns

Single-Motor Controlled Mechanically Frequency Reconfigurable Unidirectional Antenna Array with Stable Radiation Patterns

An Energy-Efficient ECG Processor with Ultra-Low-Parameter Multi-Stage Neural Network and Optimized Power-of-Two Quantization.

This work presents an energy-efficient ECG processor designed for Cardiac Arrhythmia Classification. The processor integrates a pre-processing and neural network accelerator, achieved through algorithm-hardware co-design to optimize hardware resources. We propose a lightweight two-stage neural network architecture, where the first stage includes discrete wavelet transformation and an ultra-low-parameter multilayer perceptron (MLP) network, and the second stage utilizes group convolution and channel shuffle. Both stages leverage neural networks for hardware resource reuse and feature a reconfigurable processing element array and memory blocks adapted to the proposed two-stage structure to efficiently handle various convolution and MLP layers operations in the two-stage network. Additionally, an optimized power-of-two (OPOT) quantization technique is proposed to enhance accuracy in low-bit quantization, and a multiplier-less processing element structure tailored for the OPOT weight quantization is introduced. The ECG processor was implemented on a 65nm CMOS process technology with 4KB of SRAM memory, achieving an energy consumption per interference of 0.15 uJ with a power supply of 1V, 64% energy saving compared to the recent state-of-the-art work. Under 4-bit weight precision, the 5-class ECG signal classification accuracy reached 98.59% on the MIT-BIH arrhythmia dataset.

A Multi-Layer Stacked Microfluidic Tactile Display With High Spatial Resolution.

Pneumatic tactile displays dynamically customize surface morphological features with reconfigurable arrays of independently addressable actuators. However, their ability to render detailed tactile patterns or fine textures is limited by the low spatial resolution. For pneumatic tactile displays, the high-density integration of pneumatic actuators within a small space (fingertip) poses a significant challenge in terms of pneumatic circuit wiring. In contrast to the structure with a single-layer layout of pipes, we propose a multi-layered stacked microfluidic pipe structure that allows for a higher density of actuators and retains their independent actuation capabilities. Based on the proposed structure, we developed a soft microfluidic tactile display with a spatial resolution of 1.25 mm. The device consists of a 5 × 5 array of independently addressable microactuators, driven by pneumatic pressure, each of which enables independent actuation of the surface film and continuous control of the height. At a relative pressure of 1000 mbar, the actuator produced a perceptible out-of-plane deformation of 0.145 mm and a force of 17.7 mN. User studies showed that subjects can easily distinguish eight tactile patterns with 96% accuracy.

A Novel Thermoelectric Generation Array Reconfiguration to Reduce Mismatch Power Loss Under Nonuniform Temperature Distribution

In practice, industrial exhaust emissions as well as emissions from automobiles, ships, biomass combustion, etc., can be potential application areas for thermoelectric generation (TEG). However, the structural design of heat exchange equipment is usually limited by the internal flow field, resulting in uneven temperature distribution on the heat exchange equipment’s surface. The resulting mismatch power loss is a major challenge for thermoelectric power generation. In this study, based on the characteristics of the surface temperature distribution of heat exchange equipment in the context of gas emissions, a static reconfiguration scheme is proposed for reconfiguring honeycomb (HC) arrays using the symmetric interval crossing (SIC) method. Based on a fixed interconnect array configuration, the solution requires only a change in the location of the modules and no change in the electrical connections, thus reducing mismatch losses while lowering manufacturing costs. Test experiments are conducted for 6 × 6 TEG arrays, mismatch losses are evaluated for four nonuniform temperature distribution cases, and the performance of seven different TEG array configurations is compared. The findings demonstrate that, in nonuniform temperature distribution scenarios, the SIC method can effectively reduce mismatch losses and has a greater output power than alternative array configurations.

A Scalable Hierarchical Dynamic PV Array Reconfiguration under Partial Shading

Photovoltaic (PV) arrays are often affected by partial shading (PS), which can significantly reduce their power output. Dynamic reconfiguration is a promising technique for mitigating the negative effects of PS by adjusting the electrical connections of the PV modules in real-time. This paper introduces a hierarchical-based switching block scheme for the dynamic reconfiguration of PV arrays under PS conditions. With the aim of mitigating the negative impact of PS on PV arrays, the proposed system employs a low complexity and easily scalable architecture, making it well-suited for practical applications. Comparative assessments against conventional configurations such as bridge-linked (BL), total-cross-tied (TCT), and series–parallel (SP) reveal superior energy harvesting efficiency for the proposed system under various shading conditions. The hierarchical switching block architecture, featuring multiple levels of switching blocks, enables efficient and flexible reconfiguration of the PV array, even in the presence of complex shading patterns. Through extensive simulations, the system consistently outperforms conventional configurations by adapting effectively to changing shading patterns and optimizing the PV array’s output. The proposed switching block (SB) reconfiguration technique significantly outperforms existing methods like TCT, Sudoku, dynamic proposals, and Magic Square in terms of both power generation (up to 42.52% increase) and efficiency (up to 42.13% improvement) under diverse partial shading conditions. The proposed hierarchical-based switching block scheme thus presents a promising solution for enhancing the dynamic reconfiguration of PV arrays under PS conditions, offering a balance between low complexity, scalability, and superior energy harvesting efficiency for practical applications in the realm of solar energy.

Flip : Data-centric Edge CGRA Accelerator

Coarse-Grained Reconfigurable Arrays (CGRA) are promising edge accelerators due to the outstanding balance in flexibility, performance, and energy efficiency. Classic CGRAs statically map compute operations onto the processing elements (PE) and route the data dependencies among the operations through the Network-on-Chip. However, CGRAs are designed for fine-grained static instruction-level parallelism and struggle to accelerate applications with dynamic and irregular data-level parallelism, such as graph processing. To address this limitation, we present Flip , a novel accelerator that enhances traditional CGRA architectures to boost the performance of graph applications. Flip retains the classic CGRA execution model while introducing a special data-centric mode for efficient graph processing. Specifically, it leverages the inherent data parallelism of graph algorithms by mapping graph vertices onto PEs rather than the operations and supporting dynamic routing of temporary data according to the runtime evolution of the graph frontier. Experimental results demonstrate that Flip achieves up to 36× speedup with merely 19% more area compared to classic CGRAs. Compared to state-of-the-art large-scale graph processors, Flip has similar energy efficiency and 2.2× better area efficiency at a much-reduced power/area budget.

Optimal dynamic reconfiguration of thermoelectric generator array using RIME optimizer to maximize the generated power

The innovation of green and flexible sources of electricity is identified as one of the key enablers to a more electric-dependent world aiming at meeting the technology needs and promised sustainability goals. The thermoelectric generator device is one of these innovative sources as it directly converts the wasted heat to electric power with a cheap, silent, and no-emissions process. Yet, the power output of the thermoelectric generator is primarily based on the temperature distribution on its sides. Towards efficiently saving the wasted heat and maximizing the output power of the thermoelectric generator, optimizing the configuration for the thermoelectric generator array is paramount. In this regard, this paper proposes an optimal dynamic reconfiguration for several design options of the thermoelectric generator array, including series–parallel, total-cross-tied, bridge-linked, and honey-comp, to better enhance the thermoelectric generator array output power under nonuniform temperature distribution. The proposed dynamic reconfiguration approach is mathematically formulated as a maximization optimization problem for the thermoelectric generator array output power. The physics-based optimization of RIME is tailored to provide the optimal array reconfiguration layout that realizes the maximum power, upon which the overall power loss is potentially decreased, and the energy conversion efficiency is significantly improved. The efficacy and superiority of the proposed configuration types and optimal dynamic reconfiguration technique are assessed for two sizes of the thermoelectric generator array: a symmetric 9x9 array and an asymmetric 10x15 array. A detailed analysis is conducted to compare the thermoelectric generator output power of the studied configurations without and with the proposed dynamic reconfiguration technique. The results affirm that optimized dynamic reconfiguration significantly enhances the thermoelectric generator array-generated power. One of the key observations from the results is that employing a dynamic reconfiguration for the bridge-linked type yields up to a 10% increase in the output power of the thermoelectric generator array compared to the design options without reconfiguration. Also, the results show that the dynamically reconfigured bridge-linked type could yield about a 5% increase in the produced output power compared to other dynamically reconfigured design options.

Reconfigurable moiré nanolaser arrays with phase synchronization.

Miniaturized lasers play a central role in the infrastructure of modern information society. The breakthrough in laser miniaturization beyond the wavelength scale has opened up new opportunities for a wide range of applications1-4, as well as for investigating light-matter interactions in extreme-optical-field localization and lasing-mode engineering5-19. An ultimate objective of microscale laser research is to develop reconfigurable coherent nanolaser arrays that can simultaneously enhance information capacity and functionality. However, the absence of a suitable physical mechanism for reconfiguring nanolaser cavities hinders the demonstration of nanolasers in either a single cavity or a fixed array. Here we propose and demonstrate moiré nanolaser arrays based on optical flatbands in twisted photonic graphene lattices, in which coherent nanolasing is realized from a single nanocavity to reconfigurable arrays of nanocavities. We observe synchronized nanolaser arrays exhibiting high spatial and spectral coherence, across a range of distinct patterns, including P, K and U shapes and the Chinese characters '' and '' ('China' in Chinese). Moreover, we obtain nanolaser arrays that emit with spatially varying relative phases, allowing us to manipulate emission directions. Our work lays the foundation for the development of reconfigurable active devices that have potential applications in communication, LiDAR (light detection and ranging), optical computing and imaging.

On-demand entanglement of molecules in a reconfigurable optical tweezer array

Entanglement is crucial to many quantum applications, including quantum information processing, quantum simulation, and quantum-enhanced sensing. Because of their rich internal structure and interactions, molecules have been proposed as a promising platform for quantum science. Deterministic entanglement of individually controlled molecules has nevertheless been a long-standing experimental challenge. We demonstrate on-demand entanglement of individually prepared molecules. Using the electric dipolar interaction between pairs of molecules prepared by using a reconfigurable optical tweezer array, we deterministically created Bell pairs of molecules. Our results demonstrate the key building blocks needed for quantum applications and may advance quantum-enhanced fundamental physics tests that use trapped molecules.

A fully reconfigurable 1 × 4 power divider with power division reconfiguration and size reduction based on series coupled cascaded transmission lines

This work presents a 1X4 fully reconfigurable power divider with a wide range of power divisions and reduced size, designed for adaptive null forming and beam steering networks in RF front ends. The design is based on the impedance transformation of series-coupled cascaded transmission lines and a reconfigurable isolation network. The impedance of power-dividing arms is transformed by tuning the voltage-dependent equivalent capacitor, controlling insertion loss, and indirectly influencing the power division ratio. Four transmission states with sixteen different power levels are realized. This design approach is validated by fabricating a prototype with four output ports. The measured power division varies from 6.5 dB to 18 dB at the center frequency, and the return loss is greater than -32 dB. Output port isolation is greater than -30 dB, and the phase response at output ports only shows a difference of less than 2 degrees. The power at any output port can be reconfigured independently without affecting the system’s overall performance, achieved through a series-coupled transmission line, contributing to a reduction in size. A size reduction of 37 percent is attained in this design. The proposed power divider can provide a wide range of power levels at all output ports within a narrow band around the center frequency. Hence, the design is suitable for beam steering networks, pattern, and polarization reconfigurable antenna arrays. The design also has extension capability into N-way configurations.

Logical quantum processor based on reconfigurable atom arrays

Suppressing errors is the central challenge for useful quantum computing1, requiring quantum error correction (QEC)2–6 for large-scale processing. However, the overhead in the realization of error-corrected ‘logical’ qubits, in which information is encoded across many physical qubits for redundancy2–4, poses substantial challenges to large-scale logical quantum computing. Here we report the realization of a programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits. Using logical-level control and a zoned architecture in reconfigurable neutral-atom arrays7, our system combines high two-qubit gate fidelities8, arbitrary connectivity7,9, as well as fully programmable single-qubit rotations and mid-circuit readout10–15. Operating this logical processor with various types of encoding, we demonstrate improvement of a two-qubit logic gate by scaling surface-code6 distance from d = 3 to d = 7, preparation of colour-code qubits with break-even fidelities5, fault-tolerant creation of logical Greenberger–Horne–Zeilinger (GHZ) states and feedforward entanglement teleportation, as well as operation of 40 colour-code qubits. Finally, using 3D [[8,3,2]] code blocks16,17, we realize computationally complex sampling circuits18 with up to 48 logical qubits entangled with hypercube connectivity19 with 228 logical two-qubit gates and 48 logical CCZ gates20. We find that this logical encoding substantially improves algorithmic performance with error detection, outperforming physical-qubit fidelities at both cross-entropy benchmarking and quantum simulations of fast scrambling21,22. These results herald the advent of early error-corrected quantum computation and chart a path towards large-scale logical processors.