Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition

The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread deployment. To address the problem, we leverage Dynamic Vision Sensors (DVS) cameras to detect violent incidents and preserve privacy since it captures pixel brightness variations instead of static imagery. We introduce the Bullying10K dataset, encompassing various actions, complex movements, and occlusions from real-life scenarios. It provides three benchmarks for evaluating different tasks: action recognition, temporal action localization, and pose estimation. With 10,000 event segments, totaling 12 billion events and 255 GB of data, Bullying10K contributes significantly by balancing violence detection and personal privacy persevering. And it also poses a challenge to the neuromorphic dataset. It will serve as a valuable resource for training and developing privacy-protecting video systems. The Bullying10K opens new possibilities for innovative approaches in these domains.Comment: Accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmark

FireFly: A High-Throughput and Reconfigurable Hardware Accelerator for Spiking Neural Networks

Spiking neural networks (SNNs) have been widely used due to their strong biological interpretability and high energy efficiency. With the introduction of the backpropagation algorithm and surrogate gradient, the structure of spiking neural networks has become more complex, and the performance gap with artificial neural networks has gradually decreased. However, most SNN hardware implementations for field-programmable gate arrays (FPGAs) cannot meet arithmetic or memory efficiency requirements, which significantly restricts the development of SNNs. They do not delve into the arithmetic operations between the binary spikes and synaptic weights or assume unlimited on-chip RAM resources by using overly expensive devices on small tasks. To improve arithmetic efficiency, we analyze the neural dynamics of spiking neurons, generalize the SNN arithmetic operation to the multiplex-accumulate operation, and propose a high-performance implementation of such operation by utilizing the DSP48E2 hard block in Xilinx Ultrascale FPGAs. To improve memory efficiency, we design a memory system to enable efficient synaptic weights and membrane voltage memory access with reasonable on-chip RAM consumption. Combining the above two improvements, we propose an FPGA accelerator that can process spikes generated by the firing neuron on-the-fly (FireFly). FireFly is implemented on several FPGA edge devices with limited resources but still guarantees a peak performance of 5.53TSOP/s at 300MHz. As a lightweight accelerator, FireFly achieves the highest computational density efficiency compared with existing research using large FPGA devices

Is Conventional SNN Really Efficient? A Perspective from Network Quantization

Spiking Neural Networks (SNNs) have been widely praised for their high energy efficiency and immense potential. However, comprehensive research that critically contrasts and correlates SNNs with quantized Artificial Neural Networks (ANNs) remains scant, often leading to skewed comparisons lacking fairness towards ANNs. This paper introduces a unified perspective, illustrating that the time steps in SNNs and quantized bit-widths of activation values present analogous representations. Building on this, we present a more pragmatic and rational approach to estimating the energy consumption of SNNs. Diverging from the conventional Synaptic Operations (SynOps), we champion the "Bit Budget" concept. This notion permits an intricate discourse on strategically allocating computational and storage resources between weights, activation values, and temporal steps under stringent hardware constraints. Guided by the Bit Budget paradigm, we discern that pivoting efforts towards spike patterns and weight quantization, rather than temporal attributes, elicits profound implications for model performance. Utilizing the Bit Budget for holistic design consideration of SNNs elevates model performance across diverse data types, encompassing static imagery and neuromorphic datasets. Our revelations bridge the theoretical chasm between SNNs and quantized ANNs and illuminate a pragmatic trajectory for future endeavors in energy-efficient neural computations

Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks

Spiking Neural Networks (SNNs) have received considerable attention not only for their superiority in energy efficient with discrete signal processing, but also for their natural suitability to integrate multi-scale biological plasticity. However, most SNNs directly adopt the structure of the well-established DNN, rarely automatically design Neural Architecture Search (NAS) for SNNs. The neural motifs topology, modular regional structure and global cross-brain region connection of the human brain are the product of natural evolution and can serve as a perfect reference for designing brain-inspired SNN architecture. In this paper, we propose a Multi-Scale Evolutionary Neural Architecture Search (MSE-NAS) for SNN, simultaneously considering micro-, meso- and macro-scale brain topologies as the evolutionary search space. MSE-NAS evolves individual neuron operation, self-organized integration of multiple circuit motifs, and global connectivity across motifs through a brain-inspired indirect evaluation function, Representational Dissimilarity Matrices (RDMs). This training-free fitness function could greatly reduce computational consumption and NAS's time, and its task-independent property enables the searched SNNs to exhibit excellent transferbility and scalability. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art (SOTA) performance with shorter simulation steps on static datasets (CIFAR10, CIFAR100) and neuromorphic datasets (CIFAR10-DVS and DVS128-Gesture). The thorough analysis also illustrates the significant performance improvement and consistent bio-interpretability deriving from the topological evolution at different scales and the RDMs fitness function

FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator

Spiking Neural Networks (SNNs) are expected to be a promising alternative to Artificial Neural Networks (ANNs) due to their strong biological interpretability and high energy efficiency. Specialized SNN hardware offers clear advantages over general-purpose devices in terms of power and performance. However, there's still room to advance hardware support for state-of-the-art (SOTA) SNN algorithms and improve computation and memory efficiency. As a further step in supporting high-performance SNNs on specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can address the issue of non-spike operation in current SOTA SNN algorithms, which presents an obstacle in the end-to-end deployment onto existing SNN hardware. To more effectively align with the SNN characteristics, we design a spatiotemporal dataflow that allows four dimensions of parallelism and eliminates the need for membrane potential storage, enabling on-the-fly spike processing and spike generation. To further improve hardware acceleration performance, we develop a high-performance spike computing engine as a backend based on a systolic array operating at 500-600MHz. To the best of our knowledge, FireFly v2 achieves the highest clock frequency among all FPGA-based implementations. Furthermore, it stands as the first SNN accelerator capable of supporting non-spike operations, which are commonly used in advanced SNN algorithms. FireFly v2 has doubled the throughput and DSP efficiency when compared to our previous version of FireFly and it exhibits 1.33 times the DSP efficiency and 1.42 times the power efficiency compared to the current most advanced FPGA accelerators

Preparation and Innovative Design Applications of Paper-Based Aluminized Film

The growing demand for sustainable and innovative materials in product design has spurred interest in unconventional resources. Despite this, a gap persists in the effective utilization of paper-based materials, particularly with metallic coatings, for creative applications. This study aims to address this by exploring the technical methods for applying Aluminum (Al) coatings to paper substrates. We developed paper-based aluminum coatings and combined them with corrugated cardboard to create a novel material for product development. Utilizing high-strength specialty paper as the substrate, an orthogonal experiment was conducted to identify key process parameters. Factors such as target–substrate distance, working pressure, current intensity, and coating duration were evaluated for their impact on the properties of the Al film. Our research culminated in the production of high-quality Al-plated corrugated cardboard. Capitalizing on its unique attributes, we employed a design approach that led to the creation of innovative furniture featuring structural forms like folding and insertion. This study not only introduces a new range of Al-plated corrugated cardboard products but also expands the potential applications of paper-based aluminized film in material-based product design.</jats:p

Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM_2.5 in China During 1998–2016

A comprehensive understanding of the relationships between PM2.5 concentration and socioeconomic factors provides new insight into environmental management decision-making for sustainable development. In order to identify the contributions of socioeconomic development to PM2.5, their spatial interaction and temporal variation of long time series are analyzed in this paper. Unary linear regression method, Spearman&#8217;s rank and bivariate Moran&#8217;s I methods were used to investigate spatio&#8211;temporal variations and relationships of socioeconomic factors and PM2.5 concentration in 31 provinces of China during the period of 1998&#8211;2016. Spatial spillover effect of PM2.5 concentration and the impact of socioeconomic factors on PM2.5 concentration were analyzed by spatial lag model. Results demonstrated that PM2.5 concentration in most provinces of China increased rapidly along with the increase of socioeconomic factors, while PM2.5 presented a slow growth trend in Southwest China and a descending trend in Northwest China along with the increase of socioeconomic factors. Long time series analysis revealed the relationships between PM2.5 concentration and four socioeconomic factors. PM2.5 concentration was significantly positive spatial correlated with GDP per capita, industrial added value and private car ownership, while urban population density appeared a negative spatial correlation since 2006. GDP per capita and industrial added values were the most important factors to increase PM2.5, followed by private car ownership and urban population density. The findings of the study revealed spatial spillover effects of PM2.5 between different provinces, and can provide a theoretical basis for sustainable development and environmental protection

Effects of patterned Artemisia capillaris on overland flow resistance under varied rainfall intensities in the Loess Plateau of China

In this paper simulated rainfall experiments in laboratory were conducted to quantify the effects oƒ patchy distributed Artemisia capillaris on spatial and temporal variations oƒ the Darcy-Weisbach friction coefficient (f). Different intensities oƒ 60, 90, 120, and 150 mm h-1 were applied on a bare plot (CK) and four different patched patterns: a checkerboard pattern (CP), a banded pattern perpendicular to slope direction (BP), a single long strip parallel to slope direction (LP), and a pattern with small patches distributed like the letter ‘X’ (XP). Each plot underwent two sets oƒ experiments, intact plant and root plots (the above-ground parts were removed). Results showed that mean ƒ for A. capillaris patterned treatments was 1.25-13.0 times oƒ that for CK. BP, CP, and XP performed more effectively than LP in increasing hydraulic roughness. The removal oƒ grass shoots significantly reduced f. A negative relationship was found between mean ƒ for the bare plot and rainfall intensity, whereas for grass patterned plots fr (mean ƒ in patterned plots divided by that for CK) increased exponentially with rainfall intensity. The ƒ -Re relation was best fitted by a power function. Soil erosion rate can be well described using ƒ by a power-law relationshi