Spiking neural networks (SNNs) have emerged as a promising paradigm for effective event-driven computation. However, CMOS-based SNN designs are limited by power consumption and complexity, while nonvolatile memory (NVM)-based SNN designs often lack biological characteristics and require active capacitive circuits to emulate neuronal dynamics. In this paper, we propose a thermally interplayed spin-orbit torque magnetic tunnel junction (TI-MTJ) macro that integrates core SNN functionalities. Our neuron array autonomously achieves leaky integrate-and-fire (LIF) model within the TI-MTJ device, thus improving power efficiency and simplifying circuit structure. Additionally, the proposed synaptic array provides adaptive in-situ responses based on a simplified spike-timing-dependent plasticity (STDP) rule. To enhance biological plausibility, our macro incorporates real-time spike monitoring and inhibition mechanisms. A comprehensive device-circuit-algorithm co-optimization framework validates the high performance of the TI-MTJ macro, achieving a synaptic energy consumption of 6.07fJ per spike, an inference accuracy of 97.76% on the MNIST dataset, and an energy efficiency of 22.8TOPS/W.

Compute-in-memory (CIM) reduces data movement but suffers from an accuracy–efficiency trade-off: Analog CIM (ACIM) is energy-efficient but loses accuracy and incurs higher cost at large bit-widths, while digital CIM (DCIM) supports high precision but is inefficient for low-precision tasks. To overcome these challenges, we propose an analog–digital hybrid CIM (HCIM) architecture to address this trade-off, including 1) an analog–digital hybrid 10T SRAM cell without additional transistors and a dual-capacitor-based multicycle weighting module to reduce area; 2) a successive-approximation-register (SAR) ADC with a pseudo C-2C capacitor array that can be reconfigured from an 8-bit ADC into two parallel 4-bit ADCs to improve configurability; 3) configurable weight division and computing resource allocation strategies. Simulations in a 28-nm process show that HCIM achieves 15.56 TOPS/W at 12-bit ($8+4$) with$1.33\times $and$2.35\times $efficiency improvement over DCIM and ACIM and$16\times $lower error. It achieves 27.87 TOPS/W at 8-bit and 78.13 TOPS/W at 4-bit, demonstrating superior energy efficiency, computational accuracy, and flexibility.

Computing-in-memory (CIM) macros based on static random access memory (SRAM) are meant to increase capacity while improving energy efficiency and reducing computing latency. However, traditional analog designs still face several key challenges, including long computing latency from separated computing phases, negative voltage fluctuations from massive parallel computing, and low bitcell density from additional transistors and capacitors for multiplication. On the other hand, only time-aligned inputs are supported in the works. To overcome the above challenges, this work proposes a buffer-free 7T-SRAM charge-domain CIM macro. It has four key features: 1) a compact 7T SRAM bitcell structure for high-energy efficiency; 2) a configurable input unit to support different sizes of input activations; 3) a time-row division (RD) strategy to support real-time processing and alleviate negative voltage fluctuations; and 4) a merging timing to conceal the input phase for high throughput. The fabricated 512-Kb SRAM-CIM macro in 40 nm achieves 79.3–290.4 Tops/W at 4-bit precision.

Analog computing-in-memory (ACIM) has garnered widespread attention due to its advantage of high energy efficiency. However, it faces large power and hardware costs to handle sophisticated nonlinear functions, such as the softmax, due to costly exponentiation and division. Existing digital-domain approaches often rely on dedicated modules to carry out these operations, leading to a cost expensive area and high-power consumption. To address the issues, we propose a self-calibrating analog circuitry for a softmax-scaled function with ACIM. By exploiting transistor subthreshold properties, the work eliminates expensive digital operations while mapping exponentiation and division to successive analog circuits. A self-calibration module further mitigates partial mismatch-induced deviations by dynamically tuning bias voltages, improving overall fitting accuracy and system robustness. The proposed softmax-enabled ACIM work achieves energy efficiency of 55.06–60.08TOPS/W and 684.15 GOPS/mm2at 4-bit precision. In comparison with the state-of-the-art ACIMs with softmax implications, our proposed work shows higher energy efficiency and area efficiency.

Point cloud registration, a fundamental task in computer science and artificial intelligence, involves rigidly transforming point clouds from different perspectives into a common coordinate system. Traditional registration methods often lack robustness and fail to achieve the desired level of accuracy. In contrast, deep learning-based registration methods have demonstrated improved accuracy and generalization. However, these methods are hindered by large parameter sizes, complex network architectures, and challenges related to efficiency, robustness, and partial overlaps. In this study, we propose a lightweight deep learning-based registration method that captures features from multiple perspectives to predict overlapping points and mitigate the interference of non-overlapping points. Specifically, our approach utilizes pruning and weight-sharing quantization techniques to reduce model size and simplify the network structure. We evaluate the proposed model on noisy and partially overlapping point clouds from the ModelNet40 dataset, comparing its performance against other existing methods. Experimental results show that the proposed method significantly reduces the model's parameter size without compromising registration accuracy.

Human point cloud registration is a critical problem in the fields of computer vision and computer graphics applications. Currently, due to the presence of joint hinges and limb occlusions in human point clouds, point cloud alignment is challenging. To address these two limits, this paper proposes an unsupervised non-rigid human point cloud registration method based on deformation field fusion. The method mainly consists of the deep dynamic link deformation field estimation module and the probabilistic alignment deformation field estimation module. The deep dynamic link deformation field estimation module uses a time series network to convert non-rigid deformation into multiple rigid deformations. Then, feature extraction is performed to estimate the deformation field based on the rigid deformations. The probabilistic alignment deformation field estimation module builds on a Gaussian mixture model and adds local and global constraint conditions for deformation field estimation. Finally, the two deformation fields are fused into the total deformed field by aligning them, which enhances the sensitivity to both global and local feature information. The experimental results on public datasets and real private datasets demonstrate that the proposed method has higher accuracy and better robustness under joint hinges and limb adhesion conditions.

SRAM-based computing-in-memory (SRAM-CIM) provides fast speed and good scalability with advanced process technology. However, the energy efficiency of the state-of-the-art current-domain SRAM-CIM bit-cell structure is limited and the peripheral circuitry (e.g., DAC/ADC) for high-precision is expensive. This paper proposes a charge-pulsation SRAM (CP-SRAM) structure to achieve ultra-high energy-efficiency thanks to its charge-domain mechanism. Furthermore, our proposed CP-SRAM CIM supports configurable precision (2/4/6-bit). The CP-SRAM CIM macro was designed in 180nm (with silicon verification) and 40nm (simulation) nodes. The simulation results in 40nm show that our macro can achieve energy efficiency of ~2950Tops/W at 2-bit precision, ~576.4 Tops/W at 4-bit precision and ~111.7 Tops/W at 6-bit precision, respectively.

On-orbit geometric calibration is a critical and essential step to guarantee the high geometric positioning accuracy of high-resolution optical satellite imagery. In this paper, we first review and summarize the on-orbit geometric calibration methods for high-resolution optical satellite and then analyze their advantages and disadvantages. Finally, we present our perspective on on-board geometric calibration which can be implemented automatically in real time. With the overview of geometric calibration developed in the past decades, the two conclusions could be driven up: (1) the current on-orbit geometric calibration technology based on ground control points (GCPs) has been mature, which can largely improve the geometric positioning accuracy of satellite imagery; (2) new innovation method of on-board geometric calibration for real-time improvement of satellite imagery positioning accuracy is needed. In the end, this paper presents our technique frame of on-board geometric calibration system.

At present, APD (Avalanche Photo Diode) arrays laser imaging based-on Geiger-mode has been a main research of laser radar. For low-cost and small APD arrays, we propose a distance imaging method based on APD working at Linear-mode. For this reason, the prototype of GLiDAR-II has been developed. The test shows that the max imaging range is 20 meters and the precision is better than 5 cm. This paper first introduces the principle of the prototype, its hardware framework and the principle of 3D imaging program, then focuses on the data communication, data correcting and the analysis of the real-time imaging results. Finally, in order to check the 3D image details of historical data conveniently, a program has be written, which can set historical data range, data type, scanning direction and imaging color. The results of historical imaging will be analyzed and discussed in the end.

This paper presents a FPGA (Field Programmable Gate Array)-based remotely sensed imagery denoising method, named FPGA-based median filtering. The proposed method is capable of processing large volume data since it takes full advantages of FPGA hardware and abundant logic units. This paper first overviews the traditional median filtering algorithm, and then highlights the FPGA-based filtering, including hardware components, software platform, and implementation of traditional median filtering using FPGA hardware in detail. The image frame with two dimensions of 10000×10000 pixels2is employed to test the proposed method. The comparison analysis between the proposed FPGA-based median filtering method and traditional median filtering based on ENVI version 4.8 software is carried out. The experimental result demonstrates that the proposed method is capable of saving the time more than 20 times than traditional median filtering based on ENVI version4.8 software does.

Retrievals of surface soil moisture (SSM) from remotely sensed satellites have become important in agriculture, meteorology. However, any models have been presented for different satellite imagery, which are complex with data processing cumbersome and inconvenient. This paper introduces the one model of CBERS-02B and two models of Landsat TM image. Comparing and analyzing the accuracy of the models from CBERS-02B satellite imagery. The results showed that the overall accuracy of SSM from CBERS-02B's average soil moisture content reached 91.26%. It is higher than the accuracy of the other models of Landsat TM. These results demonstrate that the model can effectively calculate the SSMs for CBERS-02B satellite imagery.