A Little Energy Goes a Long Way: Energy-Efficient, Accurate Conversion from Convolutional Neural Networks to Spiking Neural Networks

Spiking neural networks (SNNs) offer an inherent ability to process spatial-temporal data, or in other words, realworld sensory data, but suffer from the difficulty of training high accuracy models. A major thread of research on SNNs is on converting a pre-trained convolutional neural network (CNN) to an SNN of the same structure. State-of-the-art conversion methods are approaching the accuracy limit, i.e., the near-zero accuracy loss of SNN against the original CNN. However, we note that this is made possible only when significantly more energy is consumed to process an input. In this paper, we argue that this trend of "energy for accuracy" is not necessary -- a little energy can go a long way to achieve the near-zero accuracy loss. Specifically, we propose a novel CNN-to-SNN conversion method that is able to use a reasonably short spike train (e.g., 256 timesteps for CIFAR10 images) to achieve the near-zero accuracy loss. The new conversion method, named as explicit current control (ECC), contains three techniques (current normalisation, thresholding for residual elimination, and consistency maintenance for batch-normalisation), in order to explicitly control the currents flowing through the SNN when processing inputs. We implement ECC into a tool nicknamed SpKeras, which can conveniently import Keras CNN models and convert them into SNNs. We conduct an extensive set of experiments with the tool -- working with VGG16 and various datasets such as CIFAR10 and CIFAR100 -- and compare with state-of-the-art conversion methods. Results show that ECC is a promising method that can optimise over energy consumption and accuracy loss simultaneously

Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff

Spiking neural networks (SNNs), a variant of artificial neural networks (ANNs) with the benefit of energy efficiency, have achieved the accuracy close to its ANN counterparts, on benchmark datasets such as CIFAR10/100 and ImageNet. However, comparing with frame-based input (e.g., images), event-based inputs from e.g., Dynamic Vision Sensor (DVS) can make a better use of SNNs thanks to the SNNs' asynchronous working mechanism. In this paper, we strengthen the marriage between SNNs and event-based inputs with a proposal to consider anytime optimal inference SNNs, or AOI-SNNs, which can terminate anytime during the inference to achieve optimal inference result. Two novel optimisation techniques are presented to achieve AOI-SNNs: a regularisation and a cutoff. The regularisation enables the training and construction of SNNs with optimised performance, and the cutoff technique optimises the inference of SNNs on event-driven inputs. We conduct an extensive set of experiments on multiple benchmark event-based datasets, including CIFAR10-DVS, N-Caltech101 and DVS128 Gesture. The experimental results demonstrate that our techniques are superior to the state-of-the-art with respect to the accuracy and latency

PSN-PC: A Novel Antimicrobial and Anti-Biofilm Peptide from the Skin Secretion of Phyllomedusa-camba with Cytotoxicity on Human Lung Cancer Cell

Peptides derived from amphibian skin secretion are promising drug prototypes for combating widespread infection. In this study, a novel peptide belonging to the phylloseptin family of antimicrobial peptides was isolated from the skin secretion of the Phyllomedusa camba, namely phylloseptin-PC (PSN-PC). The biosynthetic precursor was obtained by molecular cloning and the mature peptide sequence was confirmed through tandem mass spectrometry (MS/MS) fragmentation sequencing in the skin secretion. The synthetic replicate exhibited a broad spectrum antimicrobial activity against Staphylococcus aureus, methicillin-resistant Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Candida albicans at concentrations of 2, 2, 8, 32 and 2 µM, respectively. It also showed the capability of eliminating S. aureus biofilm with a minimal biofilm eradication concentration of 8 µM. The haemolysis of this peptide was not significant at low concentrations but had a considerable increase at high concentrations. Additionally, this peptide showed an anti-proliferation effect on the non-small cell lung cancer cell line (NCI-H157), with low cytotoxicity on the human microvascular endothelial cell line (HMEC-1). The discovery of the novel peptide may provide useful clues for new drug discoveries

A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network.

This article conforms to a recent trend of developing an energy-efficient Spiking Neural Network (SNN), which takes advantage of the sophisticated training regime of Convolutional Neural Network (CNN) and converts a well-trained CNN to an SNN. We observe that the existing CNN-to-SNN conversion algorithms may keep a certain amount of residual current in the spiking neurons in SNN, and the residual current may cause significant accuracy loss when inference time is short. To deal with this, we propose a unified framework to equalize the output of the convolutional or dense layer in CNN and the accumulated current in SNN, and maximally align the spiking rate of a neuron with its corresponding charge. This framework enables us to design a novel explicit current control (ECC) method for the CNN-to-SNN conversion which considers multiple objectives at the same time during the conversion, including accuracy, latency, and energy efficiency. We conduct an extensive set of experiments on different neural network architectures, e.g., VGG, ResNet, and DenseNet, to evaluate the resulting SNNs. The benchmark datasets include not only the image datasets such as CIFAR-10/100 and ImageNet but also the Dynamic Vision Sensor (DVS) image datasets such as DVS-CIFAR-10. The experimental results show the superior performance of our ECC method over the state-of-the-art

Joint Trading and Scheduling among Coupled Carbon-Electricity-Heat-Gas Industrial Clusters

This paper presents a carbon-energy coupling management framework for an industrial park, where the carbon flow model accompanying multi-energy flows is adopted to track and suppress carbon emissions on the user side. To deal with the quadratic constraint of gas flows, a bound tightening algorithm for constraints relaxation is adopted. The synergies among the carbon capture, energy storage, power-to-gas further consume renewable energy and reduce carbon emissions. Aiming at carbon emissions disparities and supply-demand imbalances, this paper proposes a carbon trading ladder reward and punishment mechanism and an energy trading and scheduling method based on Lyapunov optimization and matching game to maximize the long-term benefits of each industrial cluster without knowing the prior information of random variables. Case studies show that our proposed trading method can reduce overall costs and carbon emissions while relieving energy pressure, which is important for Environmental, Social and Governance (ESG)

A Combined Molecular Cloning and Mass Spectrometric Method to Identify, Characterize, and Design Frenatin Peptides from the Skin Secretion of Litoria infrafrenata

Amphibian skin secretions are unique sources of bioactive molecules, particularly bioactive peptides. In this study, the skin secretion of the white-lipped tree frog (Litoria infrafrenata) was obtained to identify peptides with putative therapeutic potential. By utilizing skin secretion-derived mRNA, a cDNA library was constructed, a frenatin gene was cloned and its encoded peptides were deduced and confirmed using RP-HPLC, MALDI-TOF and MS/MS. The deduced peptides were identified as frenatin 4.1 (GFLEKLKTGAKDFASAFVNSIKGT) and a post-translationally modified peptide, frenatin 4.2 (GFLEKLKTGAKDFASAFVNSIK.NH2). Antimicrobial activity of the peptides was assessed by determining their minimal inhibitory concentrations (MICs) using standard model microorganisms. Through studying structure–activity relationships, analogues of the two peptides were designed, resulting in synthesis of frenatin 4.1a (GFLEKLKKGAKDFASALVNSIKGT) and frenatin 4.2a (GFLLKLKLGAKLFASAFVNSIK.NH2). Both analogues exhibited improved antimicrobial activities, especially frenatin 4.2a, which displayed significant enhancement of broad spectrum antimicrobial efficiency. The peptide modifications applied in this study, may provide new ideas for the generation of leads for the design of antimicrobial peptides with therapeutic applications

Synthesis of 1, 2, 4-triazole, 1, 3, 4-thiadiazole and 1, 3, 4-oxadiazole derivatives of 6-nitrobenzimidazole

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Electronic mechanical braking system executive mechanism design, calculation, and modeling based on dynamic control

Introduction: As science and technology develop, automobiles are moving toward intelligence and electrification and need better braking systems.Methods: To improve the braking system’s response speed and braking effect, a longitudinal dynamics control system for automobiles based on the electronic mechanical braking system was proposed, and the electronic mechanical braking system was improved through automatic disturbance rejection control.Results: The experimental results show that the time required for achieving the target clamping force in the electronic mechanical braking system using self-disturbance rejection control and proportional integral differential control is only 0.01 s, but there is an issue of excessive control in the proportional integral differential system between 0.12 s and 0.2 s, while the self-disturbance rejection controller does not have this problem. Meanwhile, regardless of the interference applied, the electronic mechanical braking system with automatic disturbance rejection control can ensure that the clamping force does not fluctuate. In the joint simulation experiment, the expected acceleration and actual acceleration can remain consistent, and if the expected braking force is 9000 N, then the actual braking force of the electronic mechanical brake (EMB) is also 9000 N.Discussion: The above results indicate that the vehicle longitudinal dynamics control system using the electronic mechanical braking system not only responds fast but also has a good braking effect, avoiding the problem of excessive control and improving the driving experience