Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Uniform resistive switching and highly stable synaptic characteristics of HfOx sandwiched TaOx-based memristor for neuromorphic system

Voltage regulative circuits, which can provide a stable voltage, is an essential part of electronic devices. Memristor, as an emerging device, has drawn attention for voltage regulative circuits design. However, almost all of them is based non-volatile switching memristor, which need complex peripheral circuit for resistance modulation. In this brief, voltage regulative circuits have been designed basing on threshold switching (TS) memristor. The TS device based voltage regulative circuit shows reliable voltage regulation effect for different input signals, such as triangle and sinusoidal voltage input signals. Furthermore, the modulable stable output level has been demonstrated with TS based voltage regulative circuit. This brief provided a new ideal for memristor based voltage regulative circuit realization. 

Threshold Switching Memristor-Based Voltage Regulative Circuit

Refractory high entropy alloys (RHEAs), which comprise high-melting-point refractory elements, have been regarded as potential candidates that can substitute nickel-based superalloys used in high-temperature applications. This study investigated the microstructure, hardness, corrosion, and oxidation behavior of the TiNbTaVW RHEA compared with commercial-grade IN718 alloy. The equiatomic TiNbTaVW RHEA was produced using an arc melting furnace. The microstructure of the as-cast RHEA consisted of a body-centered cubic solid-solution phase with a dendritic structure corresponding to the empirical phase prediction. The RHEA exhibited a higher hardness of 522±10 HV than the IN718 alloy with 301±14 HV. The higher hardness is due to solid-solution strengthening and grain refinement. The RHEA had a lower corrosion rate in 3.5 wt% NaCl (0.0003 mm/yr) and 1 M H2SO4 (0.0009 mm/yr) than the commercial IN718 alloy (0.054 mm/yr and 1.12 mm/yr, respectively). At 850°C after 15 hours and 1050°C after 15 hours, the IN718 alloy exhibited a mass gain of 4.41 mg/cm2 and 10.84 mg/cm2, respectively, while the TiNbTaVW RHEA exhibited a mass loss of −23.58 mg/cm2 and −45.92 mg/cm2, respectively, indicating that the IN718 alloy exhibited the best oxidation resistance. Thermal and growth stresses contributed to the pores, voids, cracks, and oxide layer spallation observed in the TiNbTaVW RHEA. 

Microstructure, hardness, oxidation, and corrosion behavior of TiNbTaVW refractory high entropy alloy in 3.5 wt% NaCl and 1 M H2SO4

The tutorial explores key security and functional safety challenges for Artificial Intelligence (AI) in embedded automotive systems, including aspects from adversarial attacks, long life cycles of products, and limited energy resources of automotive platforms within safety-critical environments in diverse use cases. It provides a set of recommendations for how the security and safety engineering of machine learning can address these challenges. It also provides an overview of contemporary security and functional safety engineering practices, encompassing up-to-date legislative and technical prerequisites. Finally, we identify the role of AI edge processing in enhancing security and functional safety within embedded automotive systems.

Security and Functional Safety for AI in Embedded Automotive System – A Tutorial

In this article, we analyzed the experimental data based on the TaOx memristor and found that the threshold switching (TS) characteristics are related to temperature, and its logarithmic I–V curve is in good agreement with the space charge limiting current conduction mechanism. We use this mechanism to establish a TS physical model and then use the physical model to build an LTspice model. The model data are fitted with the experimental data, which is basically consistent. Next, using the TS memristor to simulate a leaky integrate-and-fire neuron circuit, the basic dynamics are realized. By changing the external temperature of the memristor, the output frequency of the neuron will be more intense as the temperature increases. Finally, an artificial spiking neural network (SNN) was built based on this neuron circuit for MNIST recognition task. In this SNN, the input signals fused both voltage amplitude and temperature to achieve neuromorphic multimodal preprocessing and enhance the recognition accuracy. These results demonstrated the reliability of the model, which enhanced the flexibility for exploring the application of TaOx-based TS memristors.

A model of TaOx threshold switching memristor for neuromorphic computing

The analog neural network to spiking neural network (ANN-to-SNN) conversion is an effective method for improving the performance of SNNs. However, the existing mainstream conversion method (rectified linear unit, ReLU) still face the problem of weak ability for adversarial attacks. In this brief, inspired by the radial basis function and the “near enhancement and far inhibition (NEFI)” properties of biological neurons, a threshold switching (TS) memristor based radial basis spiking neuron (RBSN) circuit is proposed for the ANN-to-SNN conversion implementation. The results indicate that the RBSN circuit can effectively implement the NEFI spiking properties, which is benefit for filtering the adversarial attacks information. Furthermore, the comparison of ReLU and RBSN conversion methods based ANN-to-SNN for MNIST dataset classification task was performed. The results indicate that the RBSN shows obviously advantage than the ReLU for confronting the adversarial attacks. The accuracy of RBSN based ANN-to-SNN achieved ~80.6%, even the input data containing 40% attack information, whereas the ReLU based ANN-to SNN only achieved ~49.2%. This brief provides new ideas for the security design of neuromorphic computing systems.

Threshold Switching Memristor-Based Radial-Based Spiking Neuron Circuit for Conversion Based Spiking Neural Networks Adversarial Attack Improvement

The effects of Y or Nb addition on the oxidation behavior of 321 steel at high temperatures were investigated by scanning electron microscopy (SEM), energy spectroscopy (EDS) and X-ray diffractometer (XRD). At the same time, the oxidation mechanism and oxidation kinetics of rare earth Y or Nb addition are explored. The results show that temperature greatly influences the high-temperature oxidation resistance of the alloys, and the oxidation phenomenon of the alloy becomes more obvious as the temperature increases. Adding 0.5 wt.% Nb or 0.045 wt.% Y elements can effectively improve the oxidation resistance of 321 stainless steel at high temperatures. The addition of rare earth Y can promote the diffusion of Cr in the matrix, leading to increased Cr content in the oxide film and the eventual formation of a dense Cr2O3 film, which effectively hinders the continuation of the oxidation reaction. As a result of the Nb addition, the outward diffusion of Cr elements can be effectively inhibited, Cr and O ion bond can be strengthened, the oxidation rate can be reduced, the adhesion rate of oxide film can be increased, and the oxidation resistance of 321 stainless steel can be improved.

/pdf/high-temperature-oxidation-behaviors-of-321-steel-with-y-or-2uk2qbxa.pdf

High-Temperature Oxidation Behaviors of 321 Steel with Y or Nb Micro-Alloying

Oxide-based memristors by incorporating thermally enhanced layer (TEL) have showed great potential in electronic devices for high-efficient and high-density neuromorphic computing owing to the improvement of multilevel resistive switching. However, research on the mechanism of resistive switching regulation is still lacking. In this work, based on the method of finite element numerical simulation analysis, a bilayer oxide-based memristor Pt/HfO2 (5 nm)/Ta2O5 (5 nm)/Pt with the Ta2O5 TEL was proposed. The oxygen vacancy concentrates distribution shows that the fracture of conductive filaments (CF) is at the interface where the local temperature is the highest during the reset process. The multilevel resistive switching properties were also obtained by applying different stop voltages. The fracture gap of CF can be enlarged with the increase of the stopping voltage, which is attributed to the heat-gathering ability of the TEL. Moreover, it was found that the fracture position of oxygen CF is dependent on the thickness of TEL, which exhibits a modulation of device RS performance. These results provide a theoretical guidance on the suitability of memristor devices for use in high-density memory and brain-actuated computer systems.

Resistive switching modulation by incorporating thermally enhanced layer in HfO2-based memristor.

Spiking neuron circuits, responsible for encoding analog signals into spiking signals, are crucial for conversion-based spiking neural networks (SNNs), enabling direct integration with conventional deep learning. However, the mainstream ReLU (Rectified Linear Unit) spiking neuron circuits lack the capability to encode negative values, resulting in loss of information. In this work, a spiking neuron circuit featuring the L-ReLU (Leaky Rectified Linear Unit) function based on an optimized threshold switching (TS) memristor model with an asymmetric switching character was proposed. The adjustment of the hyperparameter β in the TS memristor model enables the realization of a flexible negative coding function. Additionally, the CIFAR-10 classification task was executed by embedding the proposed L-ReLU spiking neuron circuit in the VGG16 model. The simulation results indicate that an obvious improvement was obtained for the CIFAR-10 classification task by adopting L-ReLU. This work provides a novel approach for memristor-based neuron circuits applied in conversion-based SNN. 

L-ReLU Spiking Neuron Circuit Based On Threshold Switching Memristor for Conversion-Based Spiking Neural Networks

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