Aerodynamic coefficient prediction is pivotal in aircraft and vehicles' design, performance evaluation, and motion control. Integrating artificial neural networks into aerodynamic coefficient prediction offers a promising alternative to traditional numerical methods burdened by extensive computations and high costs. Nevertheless, this data-driven approach faces several critical challenges, which limit its further performance enhancement: i) The current research lacks a profound understanding of the complex interplay between the shape of an object and its aerodynamic characteristics. ii) The scarcity of high-quality aerodynamic data poses a significant barrier. The models trained on limited datasets lack generalization ability, struggling to accurately predict and adapt to diverse aerodynamic performance under new shapes or conditions. To overcome these challenges, we introduce an innovative framework that employs cross-attention to capture the intimate interplay between shape and flow conditions and allows for the direct utilization of pre-trained models on general shape datasets to mitigate the scarcity of aerodynamic data. Furthermore, to bolster the inference capabilities of this data-driven approach, we integrate physical information constraints into the model, leveraging them as guiding principles to enhance the model's predictive power under unknown conditions. Experimental validation demonstrates that our proposed method performs excellently in multiple aerodynamic prediction tasks. This achievement brings a new technological breakthrough to the field of aerodynamic prediction and provides robust support for the design optimization of complex systems such as aircraft and vehicles.

The brain-inspired Spiking Neural Networks (SNNs) offer a promising event-driven and low-power approach to deep learning. Self-attention (SA) mechanism, the cornerstone of the high-performance transformer architecture, enables the model to capture the relationships between different regions of an image. However, the self-attention’s quadratic complexity across long representation sequences hinders the wide application of transformers. In this work, we introduce a novel High-Low Frequency Multi-scale Multi-head Self-Attention mechanism (HL-MMSA) as well as an efficient vision transformer model named HL-ESViT. In HL-MMSA, the input feature maps are processed through high and low pathways and the HL-ESViT departs from stacking transformer blocks repeatedly, diminishing memory and computational costs. To better capture the spatial features of images, we incorporate a novel positional encoding scheme, Relative Position Embedding MultiLayer Perceptron (RPEMP). The proposed HL-ESViT achieves a tradeoff between performance and efficiency. Extensive experiments demonstrate our model’s competitive performance on static datasets CIFAR10, CIFAR100, and neuromorphic datasets DVS128 Gesture and CIFAR10-DVS.

Episodic memory is fundamental to the brain's cognitive function, but how neuronal activity is temporally organized during its encoding and retrieval is still unknown. In this article, combining hippocampus structure with a spiking neural network (SNN), a new bionic spiking temporal memory (BSTM) model is proposed to explore the encoding, formation, and retrieval of episodic memory. For encoding episodic memory, the spike-timing-dependent-plasticity (STDP) learning algorithm and a proposed minicolumn selection algorithm are used to encode each input item into several active minicolumns. For the formation of episodic memory, a sequential memory algorithm is proposed to store the contexts between items. For retrieval of episodic memory, the local retrieval algorithm and the global retrieval algorithm are proposed to retrieve sequence information, achieving multisentence prediction and multitime step prediction. All functions of BSTM are based on bionic spiking neurons, which have biological characteristics including columnar and dendritic structures, firing and receiving spikes, and delaying transmission. To test the performance of the BSTM model, the Children's Book Test (CBT) data set was used to conduct a series of experiments under different settings, including changing the number of minicolumns, neurons and sequences, modifying sequence items, etc. Compared to other sequence memory algorithms, the experimental results show that the proposed BSTM achieves higher accuracy and better robustness.

Spiking Neural Networks (SNNs), known as the third generation of the neural network, are famous for their biological plausibility and brain-like characteristics. Recent efforts further demonstrate the potential of SNNs in high-speed inference by designing accelerators with the parallelism of temporal or spatial dimensions. However, with the limitation of hardware resources, the accelerator designs must utilize off-chip memory to store many intermediate data, which leads to both high power consumption and long latency. In this paper, we focus on the data flow between layers to improve arithmetic efficiency. Based on the spike discrete property, we design a convolution-pooling(CONVP) unit that fuses the processing of the convolutional layer and pooling layer to reduce latency and resource utilization. Furthermore, for the fully-connected layer, we apply intra-output parallelism and inter-output parallelism to accelerate network inference. We demonstrate the effectiveness of our proposed hardware architecture by implementing different SNN models with the different datasets on a Zynq XA7Z020 FPGA. The experiments show that our accelerator can achieve about x28 inference speed up with a competitive power compared with FPGA implementation on MNIST dataset and a x15 inference speed up with low power compared with ASIC design on DVSGesture dataset.

Spiking Neural Network (SNN) is a promising solution for ultra-low-power hardware. Recent SNNs have reached the performance of Deep Neural Networks (DNNs) in dealing with many tasks. However, these methods often suffer from a long simulation time to achieve the accurate spike train information. In addition, these methods are contingent on a well-designed initialization to effectively transmit the gradient information. To address these issues, we propose the Internal Spiking Neuron Model (ISNM), which uses the synaptic current instead of spike trains as the carrier of information. In addition, we design a gradual surrogate gradient learning algorithm to ensure that SNNs effectively back-propagate gradient information in the early stage of training and more accurate gradient information in the later stage of training. The experiments on various network structures on CIFAR-10 and CIFAR-100 datasets show that the proposed method can exceed the performance of previous SNN methods within 5 time steps.

Spiking Neural Networks (SNNs) are receiving increasing attention due to their biological plausibility and the potential for ultra-low-power event-driven neuromorphic hardware implementation. Due to the complex temporal dynamics and discontinuity of spikes, training SNNs directly usually suffers from high computing resources and a long training time. As an alternative, SNN can be converted from a pre-trained artificial neural network (ANN) to bypass the difficulty in SNNs learning. However, the existing ANN-to-SNN methods neglect the inconsistency of information transmission between synchronous ANNs and asynchronous SNNs. In this work, we first analyze how the asynchronous spikes in SNNs may cause conversion errors between ANN and SNN. To address this problem, we propose a signed neuron with memory function, which enables almost no accuracy loss during the conversion process, and maintains the properties of asynchronous transmission in the converted SNNs. We further propose a new normalization method, named neuron-wise normalization, to significantly shorten the inference latency in the converted SNNs. We conduct experiments on challenging datasets including CIFAR10 (95.44% top-1), CIFAR100 (78.3% top-1) and ImageNet (73.16% top-1). Experimental results demonstrate that the proposed method outperforms the state-of-the-art works in terms of accuracy and inference time. The code is available at https://github.com/ppppps/ANN2SNNConversion_SNM_NeuronNorm.

Deep spiking neural network (DSNN) is a promising computational model towards artificial intelligence. It benefits from both the DNNs and SNNs through a hierarchy structure to extract multiple levels of abstraction and the event-driven computational manner to provide ultra-low-power neuromorphic implementation, respectively. However, how to efficiently train the DSNNs remains an open question because of the non-differentiable spike function that prevents the traditional back-propagation (BP) learning algorithm directly applied to DSNNs. Here, inspired by the findings from the biological neural networks, we address the above-mentioned problem by introducing neural oscillation and spike-phase information to DSNNs. Specifically, we propose an Oscillation Postsynaptic Potential (Os-PSP) and phase-locking active function, and further put forward a new spiking neuron model, namely Resonate Spiking Neuron (RSN). Based on the RSN, we propose a Spike-Level-Dependent Back-Propagation (SLDBP) learning algorithm for DSNNs. Experimental results show that the proposed learning algorithm resolves the problems caused by the incompatibility between the BP learning algorithm and SNNs, and achieves state-of-the-art performance in single spike-based learning algorithms. This work investigates the contribution of introducing biologically inspired mechanisms, such as neural oscillation and spike-phase information to DSNNs and providing a new perspective to design future DSNNs.

Bringing machines up to human-level visual processing capabilities is an attractive research topic for decades. Deep neural networks (DNNs), inspired by the hierarchical structure of the human primary visual cortex at a macroscopic level, have achieved state-of-the-art performance in many applications. However, their practical applications remain limited due to the requisition of massive computing resources. Spiking neural networks (SNNs) simulate the spike-based information process of the biological neural system from the microscopic view and hold greater potential to ultra-low-power computations. In this paper, we imitate the human visual system from both the micro and macro scales and make the following contributions: (1) Inspired by the lateral effect between real neurons, we propose a Global-Local Hybrid Spike-Timing-Dependent Plasticity (GLHSTDP) algorithm that combines STDP with lateral synaptic learning mechanism, to train the spiking neural network. (2) We construct a deep spiking neural network (DSNN) to mimic the visual information processing mechanism in the human brain. Experimental results demonstrate that the proposed DSNN model equipped with the proposed learning algorithm works in a totally spike-based manner and achieve competitive accuracies on both the Caltech 101 and the MNIST datasets.

Spiking neurons are becoming increasingly popular owing to their biological plausibility and promising computational properties. Unlike traditional rate-based neural models, spiking neurons encode information in the temporal patterns of the transmitted spike trains, which makes them more suitable for processing spatiotemporal information. One of the fundamental computations of spiking neurons is to transform streams of input spike trains into precisely timed firing activity. However, the existing learning methods, used to realize such computation, often result in relatively low accuracy performance and poor robustness to noise. In order to address these limitations, we propose a novel highly effective and robust membrane potential-driven supervised learning (MemPo-Learn) method, which enables the trained neurons to generate desired spike trains with higher precision, higher efficiency, and better noise robustness than the current state-of-the-art spiking neuron learning methods. While the traditional spike-driven learning methods use an error function based on the difference between the actual and desired output spike trains, the proposed MemPo-Learn method employs an error function based on the difference between the output neuron membrane potential and its firing threshold. The efficiency of the proposed learning method is further improved through the introduction of an adaptive strategy, called skip scan training strategy, that selectively identifies the time steps when to apply weight adjustment. The proposed strategy enables the MemPo-Learn method to effectively and efficiently learn the desired output spike train even when much smaller time steps are used. In addition, the learning rule of MemPo-Learn is improved further to help mitigate the impact of the input noise on the timing accuracy and reliability of the neuron firing dynamics. The proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. Experimental results show that the proposed method can achieve high learning accuracy with a significant improvement in learning time and better robustness to different types of noise.