Spiking Neural Networks (SNNs) offer a low-power alternative to conventional deep neural networks by leveraging their sparse, event-driven processing for both static and event-based data. While previous spiking autoencoders were energy-intensive and limited to static inputs, this work presents a novel spiking autoencoder capable of efficiently reconstructing both static and spatiotemporal data with high fidelity. To benchmark spatial pattern reconstruction, we evaluate performance on the MNIST and Fashion-MNIST datasets. Our autoencoder encodes each static input using just a single spike, reducing total energy consumption to an estimated 2.54 mJ across the entire training and testing phases. We also propose a novel decoder neuron model that enables fine-grained control over output spike timing, achieving over a 1000 × improvement in reconstruction quality compared to prior unsupervised Spike Timing Dependent Plasticity (STDP)-based methods. Additionally, we show that our model’s reconstruction performance is close to state-of-the-art autoencoders that rely on computationally intensive supervised, gradient-based convolutional networks. To validate performance on spatiotemporal data, we use the Spiking Heidelberg Digits (SHD) dataset and examine the trade-off between energy efficiency and reconstruction accuracy. Our results show that the reconstructions preserve most of the original information, while capping total energy use at 127 mJ for the full training and testing cycle. These findings lay a foundation for designing more efficient and scalable SNNs for real-world applications.

Spiking Auto-Encoders (SAEs) have the potential to greatly outperform deep learning auto-encoders in power efficiency, yet their performance remains a challenge. This work enhances both power efficiency and accuracy by reducing spike counts and introducing key innovations. We propose a novel decoder neuron model that enables precise spike timing and implement a weight-dependent Spike-Timing-Dependent Plasticity (STDP) mechanism in the encoder for better feature learning. Our architecture encodes static MNIST images using only a single spike and reconstructs spatio-temporal data from the Spiking Heidelberg Digits (SHD) dataset, optimizing the spike count for reconstruction. This substantial reduction in spike usage translates to a marked improvement in power efficiency. In addition, the average Mean Square Error (MSE) for the MNIST images was found to be 0.039, representing a 99.93% reduction from previous results. These improvements advance neuromorphic systems toward more practical, efficient applications.

Conventional surrogate Back-Propagation-Through-Time learning in Spiking Neural Networks (SNN) demands excessive energy consumption when simulating over extended time intervals. Moreover, the spike encoding process necessitates intricate hardware support, thus undermining overall efficiency. Additionally, their classification accuracies fall short in comparison to artificial neural networks due to the inherent information loss in spike translation. Therefore, there is a critical need for efficient techniques that can enhance performance without compromising accuracy. In this study, we introduce a novel learning scheme that harnesses lossless phase coding. This approach allows us to achieve minimal inference latency, requiring a maximum of at most 8 simulation steps. Furthermore, our training times exhibit significant reductions when compared to previous single-spike networks. Our experimental results demonstrate that Phase-SNN attains state-of-the-art accuracy levels, achieving 98.6% and 89.6% accuracies on the MNIST and Fashion-MNIST datasets, respectively.

Auto-encoders are capable of performing input re-construction through an encoder-decoder structure. These net-works can serve many purposes such as noise removal and anomaly detection, whilst being trained without the need for labelled data. Spiking auto-encoders can utilise asynchronous spikes to potentially improve power and simplify the required hardware. In this work, we propose an efficient spiking auto-encoder with novel error-modulated STDP learning. Our auto-encoder uses the Time To First Spike (TTFS) encoding scheme and needs to update all synaptic weights only once per input. Also, it needs only an average of 8 spikes in its hidden layer for reconstruction, leading to a very sparse and hence potentially power-efficient implementation. We demonstrate decent reconstruction ability for MNIST and the challenging Caltech Face/Motorbike datasets and achieve excellent noise removal from MNIST images.

A quantitative risk and impact assessment framework (QUIRC) is presented, to assess the security risks associated with cloud computing platforms. This framework, called QUIRC, defines risk as a combination of the Probability of a security threat event and it's Severity, measured as its Impact. Six key Security Objectives (SO) are identified for cloud platforms, and it is proposed that most of the typical attack vectors and events map to one of these six categories. Wide-band Delphi method is proposed as a scientific means to collect the information necessary for assessing security risks. Risk assessment knowledgebases could be developed specific to each industry vertical, which then serve as inputs for security risk assessment of cloud computing platforms. QUIRC's key advantage is its fully quantitative and iterative convergence approach, which enables stakeholders to comparatively assess the relative robustness of different cloud vendor offerings and approaches in a defensible manner.