Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNN is studied for the purpose of classification, under a Generalized Linear Model (GLM) probabilistic neural model that was previously considered within the computational neuroscience literature. Conventional classification rules for SNNs operate offline based on the number of output spikes at each output neuron. In contrast, a novel training method is proposed here for a first-to-spike decoding rule, whereby the SNN can perform an early classification decision once spike firing is detected at an output neuron. Numerical results bring insights into the optimal parameter selection for the GLM neuron and on the accuracy-complexity trade-off performance of conventional and first-to-spike decoding.Comment: A shorter version will be published on Proc. IEEE ICASSP 201

Model of motion route of unmanned aerial vehicles operations with obstacles avoidance

The aerodynamic model of the group of unmanned aerial vehicles in space with obstacles is investigated in this paper, the model development is based on the methods of Dubins trajectory formation and Pythagorean spatial theorem concerning hodograph. It is determined that one of the classic trajectories used for unmanned aerial vehicle maneuver from one height to another is the intersection of circular spiral projected onto the X-Y plane in the form of a circle. The problem of obstacle avoidance is determined and Dubins diagram for two unmanned aerial vehicles routes in the environment with obstacles is given. On the basis of the specified scheme the algorithm of re-laying the UAV route with curvature adjustment by means of intermediate point which is taken out on the second scheme is described. Analysis of the known solutions in the field of increasing the route stability control for unmanned aerial vehicles and electronic suppression demonstrate the importance of the problem of forming the flight routes of unmanned aerial vehicles by passing opposing enemy areas, taking into account the peculiarities of air defense and electronic warfare application. The functional scheme of UAV recognition mechanism in the conditions of radio attacks is developed and the mechanism of formation of UAV safe movement in the conditions of radio attacks based on three basic techniques is defined

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Probabilistic spiking neural networks : Supervised, unsupervised and adversarial trainings

Spiking Neural Networks (SNNs), or third-generation neural networks, are networks of computation units, called neurons, in which each neuron with internal analogue dynamics receives as input and produces as output spiking, that is, binary sparse, signals. In contrast, second-generation neural networks, termed as Artificial Neural Networks (ANNs), rely on simple static non-linear neurons that are known to be energy-intensive, hindering their implementations on energy-limited processors such as mobile devices. The sparse event-based characteristics of SNNs for information transmission and encoding have made them more feasible for highly energy-efficient neuromorphic computing architectures. The most existing training algorithms for SNNs are based on deterministic spiking neurons that limit their flexibility and expressive power. Moreover, the SNNs are typically trained based on the back-propagation method, which unlike ANNs, it becomes challenging due to the non-differentiability nature of the spike dynamics. Considering these two key issues, this dissertation is devoted to develop probabilistic frameworks for SNNs that are tailored to the solution of supervised and unsupervised cognitive tasks. The SNNs utilize rich model, flexible and computationally tractable properties of Generalized Linear Model (GLM) neuron. The GLM is a probabilistic neural model that was previously considered within the computational neuroscience literature. A novel training method is proposed for the purpose of classification with a first-to-spike decoding rule, whereby the SNN can perform an early classification decision once spike firing is detected at an output neuron. This method is in contrast with conventional classification rules for SNNs that operate offline based on the number of output spikes at each output neuron. As a result, the proposed method improves the accuracy-inference complexity trade-off with respect to conventional decoding. For the first time in the field, the sensitivity of SNNs trained via Maximum Likelihood (ML) is studied under white-box adversarial attacks. Rate and time encoding, as well as rate and first-to-spike decoding, are considered. Furthermore, a robust training mechanism is proposed that is demonstrated to enhance the resilience of SNNs under adversarial examples. Finally, unsupervised training task for probabilistic SNNs is studied. Under generative model framework, multi-layers SNNs are designed for both encoding and generative parts. In order to train the Variational Autoencoders (VAEs), the standard ML approach is considered. To tackle the intractable inference part, variational learning approaches including doubly stochastic gradient learning, Maximum A Posterior (MAP)-based, and Rao-Blackwellization (RB)-based are considered. The latter is referred as the Hybrid Stochastic-MAP Variational Learning (HSM-VL) scheme. The numerical results show performance improvements using the HSM-VL method compared to the other two training schemes

에너지 효율적 인공신경망 설계

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 최기영.최근 심층 학습은 이미지 분류, 음성 인식 및 강화 학습과 같은 영역에서 놀라운 성과를 거두고 있다. 최첨단 심층 인공신경망 중 일부는 이미 인간의 능력을 넘어선 성능을 보여주고 있다. 그러나 인공신경망은 엄청난 수의 고정밀 계산과 수백만개의 매개 변수를 이용하기 위한 빈번한 메모리 액세스를 수반한다. 이는 엄청난 칩 공간과 에너지 소모 문제를 야기하여 임베디드 시스템에서 인공신경망이 사용되는 것을 제한하게 된다. 이 문제를 해결하기 위해 인공신경망을 높은 에너지 효율성을 갖도록 설계하는 방법을 제안한다. 첫번째 파트에서는 가중 스파이크를 이용하여 짧은 추론 시간과 적은 에너지 소모의 장점을 갖는 스파이킹 인공신경망 설계 방법을 다룬다. 스파이킹 인공신경망은 인공신경망의 높은 에너지 소비 문제를 극복하기 위한 유망한 대안 중 하나이다. 기존 연구에서 심층 인공신경망을 정확도 손실없이 스파이킹 인공신경망으로 변환하는 방법이 발표되었다. 그러나 기존의 방법들은 rate coding을 사용하기 때문에 긴 추론 시간을 갖게 되고 이것이 많은 에너지 소모를 야기하게 되는 단점이 있다. 이 파트에서는 페이즈에 따라 다른 스파이크 가중치를 부여하는 방법으로 추론 시간을 크게 줄이는 방법을 제안한다. MNIST, SVHN, CIFAR-10, CIFAR-100 데이터셋에서의 실험 결과는 제안된 방법을 이용한 스파이킹 인공신경망이 기존 방법에 비해 큰 폭으로 추론 시간과 스파이크 발생 빈도를 줄여서 보다 에너지 효율적으로 동작함을 보여준다. 두번째 파트에서는 공정 변이가 있는 상황에서 동작하는 고에너지효율 아날로그 인공신경망 설계 방법을 다루고 있다. 인공신경망을 아날로그 회로를 사용하여 구현하면 높은 병렬성과 에너지 효율성을 얻을 수 있는 장점이 있다. 하지만, 아날로그 시스템은 노이즈에 취약한 중대한 결점을 가지고 있다. 이러한 노이즈 중 하나로 공정 변이를 들 수 있는데, 이는 아날로그 회로의 적정 동작 지점을 변화시켜 심각한 성능 저하 또는 오동작을 유발하는 원인이다. 이 파트에서는 ReRAM에 기반한 고에너지 효율 아날로그 이진 인공신경망을 구현하고, 공정 변이 문제를 해결하기 위해 활성도 일치 방법을 사용한 공정 변이 보상 기법을 제안한다. 제안된 인공신경망은 1T1R 구조의 ReRAM 배열과 차동증폭기를 이용한 뉴런을 이용하여 고밀도 집적과 고에너지 효율 동작이 가능하게 구성되었다. 또한, 아날로그 뉴런 회로의 공정 변이 취약성 문제를 해결하기 위해 이상적인 뉴런의 활성도와 동일한 활성도를 갖도록 뉴런의 바이어스를 조절하는 방법을 소개한다. 제안된 방법을 사용하여 32nm 공정에서 구현된 인공신경망은 3-sigma 지점에서 50% 문턱 전압 변이와 15%의 저항값 변이가 있는 상황에서도 MNIST에서 98.55%, CIFAR-10에서 89.63%의 정확도를 달성하였으며, 970 TOPS/W에 달하는 매우 높은 에너지 효율성을 달성하였다.Recently, deep learning has shown astounding performances on specific tasks such as image classification, speech recognition, and reinforcement learning. Some of the state-of-the-art deep neural networks have already gone over humans ability. However, neural networks involve tremendous number of high precision computations and frequent off-chip memory accesses with millions of parameters. It incurs problems of large area and exploding energy consumption, which hinder neural networks from being exploited in embedded systems. To cope with the problem, techniques for designing energy efficient neural networks are proposed. The first part of this dissertation addresses the design of spiking neural networks with weighted spikes which has advantages of shorter inference latency and smaller energy consumption compared to the conventional spiking neural networks. Spiking neural networks are being regarded as one of the promising alternative techniques to overcome the high energy costs of artificial neural networks. It is supported by many researches showing that a deep convolutional neural network can be converted into a spiking neural network with near zero accuracy loss. However, the advantage on energy consumption of spiking neural networks comes at a cost of long classification latency due to the use of Poisson-distributed spike trains (rate coding), especially in deep networks. We propose to use weighted spikes, which can greatly reduce the latency by assigning a different weight to a spike depending on which time phase it belongs. Experimental results on MNIST, SVHN, CIFAR-10, and CIFAR-100 show that the proposed spiking neural networks with weighted spikes achieve significant reduction in classification latency and number of spikes, which leads to faster and more energy-efficient spiking neural networks than the conventional spiking neural networks with rate coding. We also show that one of the state-of-the-art networks the deep residual network can be converted into spiking neural network without accuracy loss. The second part of this dissertation focuses on the design of highly energy-efficient analog neural networks in the presence of variations. Analog hardware accelerators for deep neural networks have taken center stage in the aspect of high parallelism and energy efficiency. However, a critical weakness of the analog hardware systems is vulnerability to noise. One of the biggest noise sources is a process variation. It is a big obstacle to using analog circuits since the variation shifts various parameters of analog circuits from the correct operating points, which causes severe performance degradation or even malfunction. To achieve high energy efficiency with analog neural networks, we propose resistive random access memory (ReRAM) based analog implementation of binarized neural networks (BNNs) with a novel variation compensation technique through activation matching (VCAM). The proposed architecture consists of 1-transistor-1-resistor (1T1R) structured ReRAM synaptic arrays and differential amplifier based neurons, which leads to high-density integration and energy efficiency. To cope with the vulnerability of analog neurons due to process variation, the biases of all neurons are adjusted in the direction that matches average output activation of ideal neurons without variation. The technique effectively restores the classification accuracy degraded by the variation. Experimental results on 32nm technology show that the proposed architecture achieves the classification accuracy of 98.55% on MNIST and 89.63% on CIFAR-10 in the presence of 50% threshold voltage variation and 15% resistance variation at 3-sigma point. It also achieves 970 TOPS/W energy efficiency with MLP on MNIST.1 Introduction 1 1.1 Deep Neural Networks with Weighted Spikes . . . . . . . . . . . . . 2 1.2 VCAM: Variation Compensation through Activation Matching for Analog Binarized Neural Networks . . . . . . . . . . . . . . . . . . . . . 5 2 Background 8 2.1 Spiking neural network . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Spiking neuron model . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Rate coding in SNNs . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 Binarized neural networks . . . . . . . . . . . . . . . . . . . . . . . 13 2.5 Resistive random access memory . . . . . . . . . . . . . . . . . . . . 18 3 RelatedWork 22 3.1 Training SNNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2 SNNs with various spike coding schemes . . . . . . . . . . . . . . . 25 3.3 BNN implementations . . . . . . . . . . . . . . . . . . . . . . . . . 28 4 Deep Neural Networks withWeighted Spikes 33 4.1 SNN with weighted spikes . . . . . . . . . . . . . . . . . . . . . . . 34 4.1.1 Weighted spikes . . . . . . . . . . . . . . . . . . . . . . . . 34 4.1.2 Spiking neuron model for weighted spikes . . . . . . . . . . . 35 4.1.3 Noise spike . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1.4 Approximation of the ReLU activation . . . . . . . . . . . . 39 4.1.5 ANN-to-SNN conversion . . . . . . . . . . . . . . . . . . . . 41 4.2 Optimization techniques . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2.1 Skipping initial input currents in the output layer . . . . . . . 45 4.2.2 The number of phases in a period . . . . . . . . . . . . . . . 47 4.2.3 Accuracy-energy trade-off by early decision . . . . . . . . . . 50 4.2.4 Consideration on hardware implementation . . . . . . . . . . 52 4.3 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.4.1 Comparison between SNN-RC and SNN-WS . . . . . . . . . 56 4.4.2 Trade-off by early decision . . . . . . . . . . . . . . . . . . . 64 4.4.3 Comparison with other algorithms . . . . . . . . . . . . . . . 67 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5 VCAM: Variation Compensation through Activation Matching for Analog Binarized Neural Networks 71 5.1 Modification of Binarized Neural Network . . . . . . . . . . . . . . . 72 5.1.1 Binarized Neural Network . . . . . . . . . . . . . . . . . . . 72 5.1.2 Use of 0 and 1 Activations . . . . . . . . . . . . . . . . . . . 72 5.1.3 Removal of Batch Normalization Layer . . . . . . . . . . . . 73 5.2 Hardware Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 75 5.2.1 ReRAM Synaptic Array . . . . . . . . . . . . . . . . . . . . 75 5.2.2 Neuron Circuit . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2.3 Issues with Neuron Circuit . . . . . . . . . . . . . . . . . . . 82 5.3 Variation Compensation . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.1 Variation Modeling . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.2 Impact of VT Variation . . . . . . . . . . . . . . . . . . . . . 87 5.3.3 Variation Compensation Techniques . . . . . . . . . . . . . . 88 5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . 93 5.4.2 Accuracy of the Modified BNN Algorithm . . . . . . . . . . 94 5.4.3 Variation Compensation . . . . . . . . . . . . . . . . . . . . 95 5.4.4 Performance Comparison . . . . . . . . . . . . . . . . . . . . 99 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 6 Conclusion 102Docto

Fast flux botnet detection based on adaptive dynamic evolving spiking neural network

A botnet, a set of compromised machines controlled distantly by an attacker, is the basis of numerous security threats around the world. Command and Control (C&C) servers are the backbone of botnet communications, where the bots and botmaster send reports and attack orders to each other, respectively. Botnets are also categorised according to their C&C protocols. A Domain Name System (DNS) method known as Fast-Flux Service Network (FFSN) is a special type of botnet that has been engaged by bot herders to cover malicious botnet activities, and increase the lifetime of malicious servers by quickly changing the IP addresses of the domain name over time. Although several methods have been suggested for detecting FFSNs domains, nevertheless they have low detection accuracy especially with zero-day domain, quite a long detection time, and consume high memory storage. In this research we propose a new system called Fast Flux Killer System (FFKA) that has the ability to detect “zero-day” FF-Domains in online mode with an implementation constructed on Adaptive Dynamic evolving Spiking Neural Network (ADeSNN) and in an offline mode to enhance the classification process which is a novelty in this field. The adaptation includes the initial weight, testing criteria, parameters customization, and parameters adjustment. The proposed system is expected to detect fast flux domains in online mode with high detection accuracy and low false positive and false negative rates respectively. It is also expected to have a high level of performance and the proposed system is designed to work for a lifetime with low memory usage. Three public datasets are exploited in the experiments to show the effects of the adaptive ADeSNN algorithm, two of them conducted on the ADeSNN algorithm itself and the last one on the process of detecting fast flux domains. The experiments showed an improved accuracy when using the proposed adaptive ADeSNN over the original algorithm. It also achieved a high detection accuracy in detecting zero-day fast flux domains that was about (99.54%) in an online mode, when using the public fast flux dataset. Finally, the improvements made to the performance of the adaptive algorithm are confirmed by the experiments