Accurate and energy-efficient classification with spiking random neural network: corrected and expanded version

Artificial Neural Network (ANN) based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running more bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network (RNN), a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of a spiking neural network. This is demonstrated on a number of real-world classification datasets

Age of Information of a Server with Energy Requirements

We investigate a system with Poisson arrivals to two queues. One queue stores the status updates of the process of interest (or data packets) and the other handles the energy that is required to deliver the updates to the monitor. We consider that the energy is represented by packets of discrete unit. When an update ends service, it is sent to the energy queue and, if the energy queue has one packet, the update is delivered successfully and the energy packet disappears; however, in case the energy queue is empty, the update is lost. Both queues can handle, at most, one packet and the service time of updates is exponentially distributed. Using the Stochastic Hybrid System method, we characterize the average Age of Information of this system. Due to the difficulty of the derived expression, we also explore approximations of the average Age of Information of this systemJosu Doncel has received funding from the Department of Education of the Basque Government through the Consolidated Research Group MATHMODE (IT1294-19), from the Marie Sklodowska-Curie grant agreement No. 777778 and from from the Spanish Ministry of Science and Innovation with reference PID2019-108111RB-I00 (FEDER/AEI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Modeling Energy Packets Networks in the Presence of Failures

We model networks of Energy Packets which have been previously introduced by Gelenbe and his colleagues to represent the interactions between communication units and energy units in data processing networks with energy harvesting. We consider failures of batteries and the network structure models the connectivity. The model explicitly represents the amount of Energy Packets needed to transfer a Data Packet. We consider both Data Packets and Jumbo Data Packets which require distinct amounts of energy to be transmitted. Unlike previous models, our approach is based on the assumption that the transmission time of a Data Packet can be neglected when we model Energy Packets harvesting and Leakage which are operating on a larger time scale. We prove that the network of queues associated with the batteries has a product form steady-state distribution under usual Markovian assumptions. An important feature of our model is the ability to study Data Packet losses due to the lack of energy at certain nodes or to the failure of the components which cannot be obtained in previous models with closed form solutions