Neural network software
Can neural networks predict software defects?
The reviewed studies proved that the CNN and Recur- rent Neural Network (RNN) were able to learn high-level representations for software defect prediction. Moreover, they have shown that graph-based code representations, such as AST and CFG, could represent semantic and syn- tactic information.
What is a Supervised neural network (SNN)?
SNNs can implement di erent machine learning approaches such as supervised learning , unsupervised learning , reinforcement learning , and lifelong learning . In an SNN, neurons are connected via synapses. A neuron can be implemented as an integrate-and- re (IF) logic , which is illustrated in Figure 1 (left).
Can convolutional neural networks be used in deep learning?
In deep learning, convolutional neural networks (CNN) are particularly useful in the domain of computer vision for image classifications and recognition. Despite their excellent performance, there are many security issues when applying CNN models.
What is a system software for Neuromorphic hardware?
A system software for neuromorphic hardware needs to consider application property, especially the spike timing and their distortion to ensure that the SNN performance obtained on the hardware implementation matches closely to what is simulated in an application-level simulator such as CARLsim , NEST , Brian , and NEURON .
[PDF File]Intelligent routing using convolutional neural network in ...
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To overcome these limitations, Convolutional Neural Net- work (CNN) deep learning model is proposed in this paper to improve the routing computation in SD-DCN, i.e., FAT-tree topology. The CNN deep learning model gives intelligent paths according to online training of trafic patterns.
[PDF File]Software Defect Prediction Using Artificial Neural Networks ...
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Arti‰cial Neural Network (ANN) is considered as one of the widely used machine learning techniques, which is included in most of the proposed defect prediction frameworks and models. ’is research provides a critical analysis of the latest literature, published from year 2015 to
[PDF File]zkCNN: Zero Knowledge Proofs for Convolutional Neural Network ...
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zero knowledge convolutional neural network system, named zkCNN. We test it on large CNNs such as VGG16 with 15 mil-lion parameters on the CIFAR-10 dataset [31]. It takes 88.3s to generate a proof of prediction, and 59.3ms to verify the proof. The proof size is 341KB. The prover time is 1264×faster than the existing scheme in [34].
[PDF File]The application of neural network for software vulnerability ...
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phenomenon as the perception gap by examining and reviewing the early software vulnerability detection approaches. Then, from the perspective of the perception gap, this paper profoundly explores the current software vulnerability
NNCodec: An Open Source Software Implementation of the Neural ...
Figure 1. NNCodec software architecture and packages. dard (ISO,2022), for which this paper presents an open source software implementation. Main contributions are: •High-level NNCodec architecture description. • Fully standard-compliant implementation of the ISO/IEC Neural Network Coding (NNC) standard.
Implementing Spiking Neural Networks on Neuromorphic ...
software frameworks that can implement machine learning applications on current and emerging neuromorphic systems, and simultaneously address performance, energy, and reliability. Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design. We highlight challenges
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