In December 2019, the outbreak of a new coronavirus-caused pneumonia (COVID-19) in Wuhan attracted close attention in China and the world. The Chinese government took strong national intervention measures on January 23 to control the spread of the epidemic. We are trying to show the impact of these controls on the spread of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The epidemic control measures taken by the Chinese government, especially the city closure measures, reduced the scale of the COVID-19 epidemic.

The neuromorphic approach of fulfilling brain-like edge intelligence is confronted with three paramount challenges: 1) ever-increasing application demands versus insufficient on-chip resources; 2) diverse data sources and models versus heterogeneous computing paradigms; and 3) adaptation to real scenarios versus absence of on-chip learning. To bridge those gaps in the current research community, this article presents PAICORE, a 537.98-mm $^{2}$ digital neuromorphic processor with a unified computing and learning paradigm. PAICORE is a scalable 1024-core design, integrating over 1.919 million neurons and 4.773 billion synapses on a single chip. PAICORE is implemented in globally asynchronous locally synchronous (GALS) style with a five-level fat up–down quadtree as dedicated network-on-chip (NoC) infrastructure and tiled 2-D chip array as inter-chip architecture. The distributed processing cores fuse the hybrid spiking neural network (SNN), artificial neural network (ANN), and on-chip spike-timing-dependent plasticity (STDP) learning models into a unified description by modular-level reconfiguration. PAICORE achieves the peak performance of 20.74 TSOPS, 41.49 TOPS, and 115.25 GSOPS, with the best energy efficiency of 5.181 TSOPS/W, 10.372 TOPS/W, and 1.222 TSOPS/W for SNN, ANN, and on-chip learning paradigms, respectively. Benefiting from the PAIFLOW software framework, PAICORE hardware platform can be equivalently simulated and efficiently programmed in a large-scale deployment, in accordance with specific optimizing demands for its target multi-paradigm tasks.

As the third‐generation network succeeding artificial neural networks (ANNs), spiking neural networks (SNNs) offer high robustness and low energy consumption. Inspired by biological systems, the limitations of low robustness and high‐power consumption in deep reinforcement learning (DRL) are addressed by introducing SNNs. The Brain Q‐network (BrainQN) is proposed, which replaces the neurons in the classic Deep Q‐learning (DQN) algorithm with SNN neurons. BrainQN is trained using surrogate gradient learning (SGL) and ANN‐to‐SNN conversion methods. Robustness tests with input noise reveal BrainQN's superior performance, achieving an 82.14% increase in rewards under low noise and 71.74% under high noise compared to DQN. These findings highlight BrainQN's robustness and superior performance in noisy environments, supporting its application in complex scenarios. SGL‐trained BrainQN is more robust than ANN‐to‐SNN conversion under high noise. The differences in network output correlations between noisy and original inputs, along with training algorithm distinctions, explain this phenomenon. BrainQN successfully transitioned from a simulated Pong environment to a ball‐catching robot with dynamic vision sensors (DVS). On the neuromorphic chip PAICORE, it shows significant advantages in latency and power consumption compared to Jetson Xavier NX.

The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. To contain its spread, China adopted unprecedented nationwide interventions on January 23. We sought to show how these control measures impacted the containment of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23, 2020, was indispensable in reducing the eventual COVID-19 epidemic size.

Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips to support HNNs, but developing platforms to advance neuromorphic computing systems is equally essential. This paper presents the PAIBoard platform, which is designed to facilitate the implementation of HNNs. The platform comprises three main components: the upper computer, the communication module, and the neuromorphic computing chip. Both hardware and software performance measurements indicate that our platform achieves low power consumption, high energy efficiency and comparable task accuracy. Furthermore, PAIBoard is applied in a robot dog for tracking and obstacle avoidance system. The tracking module combines data from ultra-wide band (UWB) transceivers and vision, while the obstacle avoidance module utilizes depth information from an RGB-D camera, which further underscores the potential of our platform to tackle challenging tasks in real-world applications.

Due to the low power consumption and event-driven characteristics, spiking neural networks (SNNs) have garnered significant interest in recent years. In comparison, convolutional neural networks (CNNs) achieve state-of-the-art results in image classification tasks by virtue of their local perception and hierarchical learning characteristics. The fusion computing of SNN and CNN represents a promising direction that can fully leverage the low-power advantages of SNN and the high-precision advantages of CNN. In this breif, a 28-nm efficient heterogeneous CNN/SNN accelerator, named EA-HWP is proposed. EA-HWP employs a heterogeneous distributed design for the efficient on-chip implementation of CNN computation, CNN-SNN conversion, and SNN computation. Meanwhile, based on the characteristics of weight distribution, dense-and-sparse computing architecture is proposed for hybrid weight precision computation. Fabricated in 28-nm CMOS process, EA-HWP can run at a maximum of 1152MHz for CNN and 792MHz for SNN. When the supply voltage is 0.625V, EA-HWP can achieve energy efficiency of 0.14pJ/OP for CNN and 0.26pJ/SOP for SNN, respectively.