arXiv subjects

Waleed El-Geresy

Publications and source records attributed to Waleed El-Geresy.

5 recordsLinked to original sources

arXiv2025

Adversarial Jamming for Autoencoder Distribution Matching

We propose the use of adversarial wireless jamming to regularise the latent space of an autoencoder to match a diagonal Gaussian distribution. We consider the minimisation of a mean squared error distortion, where a jammer attempts to disrupt the recovery of a Gaussian source encoded and transmitted over the adversarial channel. A straightforward consequence of existing theoretical results is the fact that the saddle point of a minimax game - involving such an encoder, its corresponding decoder, and an adversarial jammer - consists of diagonal Gaussian noise output by the jammer. We use this result as inspiration for a novel approach to distribution matching in the latent space, utilising jamming as an auxiliary objective to encourage the aggregated latent posterior to match a diagonal Gaussian distribution. Using this new technique, we achieve distribution matching comparable to standard variational autoencoders and to Wasserstein autoencoders. This approach can also be generalised to other latent distributions.

cs.LG↗

arXiv2025

State Characterisation of Self-Directed Channel Memristive Devices

Knowing how to reliably use memristors as information storage devices is crucial not only to their role as emerging memories, but also for their application in neural network acceleration and as components of novel neuromorphic systems. In order to better understand the dynamics of information storage on memristors, it is essential to be able to characterise and measure their state. To this end, in this paper we propose a general, physics-inspired modelling approach for characterising the state of self-directed channel (SDC) memristors. Additionally, to enable the identification of the proposed state from device data, we introduce a noise-aware approach to the minimum-variance estimation of the state from voltage and current pairs.

cs.ET↗

arXiv2024

Energy-Constrained Information Storage on Memristive Devices in the Presence of Resistive Drift

In this paper, we examine the problem of information storage on memristors affected by resistive drift noise under energy constraints. We introduce a novel, fundamental trade-off between the information lifetime of memristive states and the energy that must be expended to bring the device into a particular state. We then treat the storage problem as one of communication over a noisy, energy-constrained channel, and propose a joint source-channel coding (JSCC) approach to storing images in an analogue fashion. To design an encoding scheme for natural images and to model the memristive channel, we make use of data-driven techniques from the field of deep learning for communications, namely deep joint source-channel coding (DeepJSCC), employing a generative model of resistive drift as a computationally tractable differentiable channel model for end-to-end optimisation. We introduce a modified version of generalised divisive normalisation (GDN), a biologically inspired form of normalisation, that we call conditional GDN (cGDN), allowing for conditioning on continuous channel characteristics, including the initial resistive state and the delay between storage and reading. Our results show that the delay-conditioned network is able to learn an energy-aware coding scheme that achieves a higher and more balanced reconstruction quality across a range of storage delays.

cs.ET↗

arXiv2024

Delay Conditioned Generative Modelling of Resistive Drift in Memristors

The modelling of memristive devices is an essential part of the development of novel in-memory computing systems. Models are needed to enable the accurate and efficient simulation of memristor device characteristics, for purposes of testing the performance of the devices or the feasibility of their use in future neuromorphic and in-memory computing architectures. The consideration of memristor non-idealities is an essential part of any modelling approach. The nature of the deviation of memristive devices from their initial state, particularly at ambient temperature and in the absence of a stimulating voltage, is of key interest, as it dictates their reliability as information storage media - a property that is of importance for both traditional storage and neuromorphic applications. In this paper, we investigate the use of a generative modelling approach for the simulation of the delay and initial resistance-conditioned resistive drift distribution of memristive devices. We introduce a data normalisation scheme and a novel training technique to enable the generative model to be conditioned on the continuous inputs. The proposed generative modelling approach is suited for use in end-to-end training and device modelling scenarios, including learned data storage applications, due to its simulation efficiency and differentiability.

cs.ET↗

arXiv2024

Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic Computing

In this paper, we build a general modelling framework for memristors, suitable for the simulation of event-based systems such as hardware spiking neural networks, and more generally, neuromorphic computing systems composed of three independent components: i) an event-based modelling approach, extending and generalising an existing general model of memristors - the Generalised Metastable Switch Model (GMSM) - eliminating errors associated with discrete time approximation, as well as offering potential improvements in terms of suitability for neuromorphic memristive system simulations; ii) a volatility state variable to allow for the unified understanding of disparate non-linear and volatile phenomena, including state relaxation, structural disruption, Joule heating, and non-linear drift in different memristive devices; and iii) a readout equation that separates the latent state variable evolution from explicit variables of interest such as an instantaneous resistance. We exhibit an illustrative implementation of this framework, fit to a resistive drift dataset for titanium dioxide memristors, based on a proposed linear conductance model for resistive drift in the devices. Finally, we highlight the application of the model to neuromorphic computing, through demonstrating the contribution of the volatility state variable to switching dynamics, resulting in frequency-dependent switching (for stable memristors acting as programmable synaptic weights) and the generation of action potentials (for unstable memristors, acting as spike-generators).

cs.ET↗