arXiv subjects

David Griffin

Publications and source records attributed to David Griffin.

10 recordsLinked to original sources

arXiv2024

RingSim- An Agent-based Approach for Modelling Mesoscopic Magnetic Nanowire Networks

We describe 'RingSim', a phenomenological agent-based model that allows numerical simulation of magnetic nanowire networks with areas of hundreds of micrometers squared for durations of hundreds of seconds; a practical impossibility for general-purpose micromagnetic simulation tools. In RingSim, domain walls (DWs) are instanced as mobile agents which respond to external magnetic fields, and their stochastic interactions with pinning sites and other DWs are described via simple phenomenological rules. We first present a detailed description of the model and its algorithmic implementation for simulating the behaviours of arrays of interconnected ring-shaped nanowires, which have previously been proposed as hardware platforms for unconventional computing applications. The model is then validated against a series of experimental measurements of an array's static and dynamic responses to rotating magnetic fields. The robust agreement between the modelled and experimental data demonstrates that agent-based modelling is a powerful tool for exploring mesoscale magnetic devices, enabling time scales and device sizes that are inaccessible to more conventional magnetic simulation techniques.

cond-mat.mes-hall↗

arXiv2024

ReStorEdge: An edge computing system with reuse semantics

This paper investigates an edge computing system where requests are processed by a set of replicated edge servers. We investigate a class of applications where similar queries produce identical results. To reduce processing overhead on the edge servers we store the results of previous computations and return them when new queries are sufficiently similar to earlier ones that produced the results, avoiding the necessity of processing every new query. We implement a similarity-based data classification system, which we evaluate based on real-world datasets of images and voice queries. We evaluate a range of orchestration strategies to distribute queries and cached results between edge nodes and show that the throughput of queries over a system of distributed edge nodes can be increased by 25-33%, increasing its capacity for higher workloads.

cs.ET↗

arXiv2024

Noise-Aware Training of Neuromorphic Dynamic Device Networks

Physical computing has the potential to enable widespread embodied intelligence by leveraging the intrinsic dynamics of complex systems for efficient sensing, processing, and interaction. While individual devices provide basic data processing capabilities, networks of interconnected devices can perform more complex and varied tasks. However, designing networks to perform dynamic tasks is challenging without physical models and accurate quantification of device noise. We propose a novel, noise-aware methodology for training device networks using Neural Stochastic Differential Equations (Neural-SDEs) as differentiable digital twins, accurately capturing the dynamics and associated stochasticity of devices with intrinsic memory. Our approach employs backpropagation through time and cascade learning, allowing networks to effectively exploit the temporal properties of physical devices. We validate our method on diverse networks of spintronic devices across temporal classification and regression benchmarks. By decoupling the training of individual device models from network training, our method reduces the required training data and provides a robust framework for programming dynamical devices without relying on analytical descriptions of their dynamics.

cs.LG↗

arXiv2023

Reservoir Computing with Magnetic Thin Films

Advances in artificial intelligence are driven by technologies inspired by the brain, but these technologies are orders of magnitude less powerful and energy efficient than biological systems. Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged with the potential to exploit natural phenomena and gain efficiency, in a similar manner to biological systems. Physical reservoir computing demonstrates this with a variety of unconventional systems, from optical-based to memristive systems. Reservoir computers provide a nonlinear projection of the task input into a high-dimensional feature space by exploiting the system's internal dynamics. A trained readout layer then combines features to perform tasks, such as pattern recognition and time-series analysis. Despite progress, achieving state-of-the-art performance without external signal processing to the reservoir remains challenging. Here we perform an initial exploration of three magnetic materials in thin-film geometries via microscale simulation. Our results reveal that basic spin properties of magnetic films generate the required nonlinear dynamics and memory to solve machine learning tasks (although there would be practical challenges in exploiting these particular materials in physical implementations). The method of exploration can be applied to other materials, so this work opens up the possibility of testing different materials, from relatively simple (alloys) to significantly complex (antiferromagnetic reservoirs).

cs.ET↗

arXiv2023

Smartphone-based Optical Sectioning (SOS) Microscopy with A Telecentric Design for Fluorescence Imaging

We proposed a Smartphone-based Optical Sectioning (SOS) microscope based on the HiLo technique, with a single smartphone replacing a high-cost illumination source and a camera sensor.We built our SOS with off-the-shelf optical mechanical cage systems with 3D-printed adapters to integrate the smartphone with the SOS main body seamlessly.The liquid light guide can be integrated with the adapter, guiding the smartphone LED light to the digital mirror device with neglectable loss.We used an electrically tunable lens (ETL) instead of a mechanical translation stage to realize low-cost axial scanning. The ETL was conjugated to the objective lens back pupil plane (BPP) to construct a telecentric design by a 4f configuration. This can exempt images of different layers from the variation in magnification. SOS has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. The broadband smartphone LED torch can effectively excite fluorescent polystyrene (PS) beads. We successfully used SOS for high contrast fluorescent PS beads imaging with different wavelengths and optical sectioning imaging of accumulated fluorescent PS beads. To our knowledge, the proposed SOS is the first smartphone-based HiLo optical sectioning microscopy. It is a powerful, low-cost tool for biomedical research in resource-limited areas.

physics.optics↗

arXiv2022

A perspective on physical reservoir computing with nanomagnetic devices

Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.

cs.ET↗

arXiv2022

Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics

Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings' computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir's output to include multiple, concurrent measures of the ring arrays magnetic states.

cond-mat.mes-hall↗

arXiv2014

TARDIS: Stably shifting traffic in space and time (extended version)

This paper describes TARDIS (Traffic Assignment and Retiming Dynamics with Inherent Stability) which is an algorithmic procedure designed to reallocate traffic within Internet Service Provider (ISP) networks. Recent work has investigated the idea of shifting traffic in time (from peak to off-peak) or in space (by using different links). This work gives a unified scheme for both time and space shifting to reduce costs. Particular attention is given to the commonly used 95th percentile pricing scheme. The work has three main innovations: firstly, introducing the Shapley Gradient, a way of comparing traffic pricing between different links at different times of day; secondly, a unified way of reallocating traffic in time and/or in space; thirdly, a continuous approximation to this system is proved to be stable. A trace-driven investigation using data from two service providers shows that the algorithm can create large savings in transit costs even when only small proportions of the traffic can be shifted.

cs.NI↗

arXiv2013

Distributed Overlay Anycast Table using Space filling curves

In this paper we present the \emph{Distributed Overlay Anycast Table}, a structured overlay that implements application-layer anycast, allowing the discovery of the closest host that is a member of a given group. One application is in locality-aware peer-to-peer networks, where peers need to discover low-latency peers participating in the distribution of a particular file or stream. The DOAT makes use of network delay coordinates and a space filling curve to achieve locality-aware routing across the overlay, and Bloom filters to aggregate group identifiers. The solution is designed to optimise both accuracy and query time, which are essential for real-time applications. We simulated DOAT using both random and realistic node distributions. The results show that accuracy is high and query time is low.

cs.NI↗

arXiv2013

Scalable peer-to-peer streaming for live entertainment content

We present a system for streaming live entertainment content over the Internet originating from a single source to a scalable number of consumers without resorting to centralised or provider- provisioned resources. The system creates a peer-to-peer overlay network, which attempts to optimise use of existing capacity to ensure quality of service, delivering low start-up delay and lag in playout of the live content. There are three main aspects of our solution. Firstly, a swarming mechanism that constructs an overlay topology for minimising propagation delays from the source to end consumers. Secondly, a distributed overlay anycast system that uses a location-based search algorithm for peers to quickly find the closest peers in a given stream. Finally, a novel incentives mechanism that encourages peers to donate capacity even when the user is not actively consuming content.

cs.NI↗