A5039574409- Logic, programming, and type systems
- Numerical Methods and Algorithms
- Advanced Memory and Neural Computing
- Formal Methods in Verification
- Computability, Logic, AI Algorithms
- Neural dynamics and brain function
- Ferroelectric and Negative Capacitance Devices
- Neuroscience and Neural Engineering
- Parallel Computing and Optimization Techniques
- Mathematical and Theoretical Analysis
- CCD and CMOS Imaging Sensors
- Software Reliability and Analysis Research
- Logic, Reasoning, and Knowledge
- Distributed and Parallel Computing Systems
- Software Engineering Research
- Bayesian Modeling and Causal Inference
- Embedded Systems Design Techniques
- Model-Driven Software Engineering Techniques
- Algorithms and Data Compression
- Scientific Computing and Data Management
- Attachment and Relationship Dynamics
- Polynomial and algebraic computation
- Manufacturing Process and Optimization
- Modular Robots and Swarm Intelligence
- Neural Networks and Reservoir Computing
University of Manchester
2008-2019
Stockton University
2001
Drexel University
1995
Middlesex University
1989-1993
Oxford Research Group
1987
Brandeis University
1967
Yale University
1940-1960
SpiNNaker (a contraction of Spiking Neural Network Architecture) is a million-core computing engine whose flagship goal to be able simulate the behavior aggregates up billion neurons in real time. It consists an array ARM9 cores, communicating via packets carried by custom interconnect fabric. The are small (40 or 72 bits), and their transmission brokered entirely hardware, giving overall extremely high bisection bandwidth over 5 packets/s. Three principal axioms parallel machine design...
The modelling of large systems spiking neurons is computationally very demanding in terms processing power and communication. SpiNNaker - Spiking Neural Network architecture a massively parallel computer system designed to provide cost-effective flexible simulator for neuroscience experiments. It can model up billion trillion synapses biological real time. basic building block the Chip Multiprocessor (CMP), which custom-designed globally asynchronous locally synchronous (GALS) with 18 ARM968...
SpiNNaker is a novel chip - based on the ARM processor which designed to support large scale spiking neural networks simulations. In this paper we describe some of features that permit chips be connected together form scalable massively-parallel systems. Our eventual goal able simulate consisting 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> neurons running in dasiareal timepsila, by mean similarly sized collection biological would...
The digital neuromorphic hardware SpiNNaker has been developed with the aim of enabling large-scale neural network simulations in real time and low power consumption. Real-time performance is achieved 1 ms integration steps, thus applies to networks for which faster scales dynamics can be neglected. By slowing down simulation, shorter steps hence scales, are often biologically relevant, incorporated. We here describe first full-scale a cortical microcircuit biological on SpiNNaker. Since...
This work presents sPyNNaker 4.0.0, the latest version of software package for simulating PyNN-defined spiking neural networks (SNNs) on SpiNNaker neuromorphic platform. Operations underpinning realtime SNN execution are presented, including an event-based operating system facilitating efficient time-driven neuron state updates and pipelined event-driven spike processing. Preprocessing, execution, neuron/synapse model implementations discussed, all in context a simple example SNN. Simulation...
Real number calculations on elementary functions are remarkably difficult to handle in mechanical proofs. In this paper, we show how these can be performed within a theorem prover or proof assistant convenient and highly automated as well interactive way. First, formally establish upper lower bounds for functions. Then, based bounds, develop rational interval arithmetic where real take place an algebraic setting. order reduce the dependency effect of arithmetic, integrate two techniques:...
Many models of spiking neural networks heavily rely on exponential waveforms. On neuromorphic multiprocessor systems like SpiNNaker, they have to be approximated by dedicated algorithms, often dominating the processing load. Here we present a processor extension for fast calculation exponentials, aimed at integration in next-generation SpiNNaker system. Our implementation achieves single-LSB precision 32bit fixed-point format and 250Mexp/s throughput 0.44nJ/exp nominal supply (1.0V), or...
SpiNNaker is a massively parallel distributed architecture primarily focused on real time simula- tion of spiking neural networks. The largest realisation the consists one million general purpose processors, making it neuromorphic computing platform in world at present time. Utilising these processors efficiently requires expert knowledge archi- tecture to generate executable code and harness potential unique inter-processor communications infra-structure that lies heart architecture. This...
It was proposed that two factors operate in mate selection: (a) homogamous trait matching so a will be chosen who resembles the self and (b) where actual-self differs from ideal-self, seen as resembling ideal-self rather than actual-self. These hypotheses were tested sample of 50 engaged girls asked to rate their actual-self, fiance's using an adjective check-list. Both supported.
Abstract An important step in many compilers for functional languages is lambda lifting. In his thesis, Hughes showed that by doing lifting a particular way, useful property called full laziness can be preserved. Full has been seen as intertwined with ever since. We show that, on the contrary, regarded completely separate process to lifting, thus making it easy use different lifters following full‐laziness transformation, or transformation which do not require On we present complete code our...
Every lazy functional programmer knows about the following approach to enumerating positive rationals: generate a two-dimensional matrix (an infinite list of lists), then traverse its finite diagonals lists).
We present a dynamic voltage and frequency scaling technique within SoCs for per-core power management: the architecture allows individual, self triggered performance-level of processing elements (PEs) less than 100ns. This enables each core to adjust its local supply depending on current computational load. A test chip has been implemented in 28nm CMOS technology, as prototype SpiNNaker2 neuromorphic many system, containing 4 PEs which are operational range 1.1V down 0.7V at frequencies...
With neuromorphic hardware rapidly moving towards large-scale, possibly immovable systems capable of implementing brain-scale neural models in hardware, there is an emerging need to be able integrate multi-system combinations sensors and cortical processors over distributed, multisite configurations. If were a standard, direct interface allowing large communicate using native signalling, it would possible use heterogeneous resources efficiently according their task suitability. We propose...
Topology can seem too abstract to be useful when first encountered. My aim in this paper is show that --- on the contrary it vital building block for continuous mathematics. In particular, if a proof undertaken at level of topology, almost always simpler there than within context specific classes topology such as those metric spaces or Domain Theory.
Neuromorphic chips are used to model biologically inspired Spiking-Neural-Networks (SNNs) where most models based on differential equations. Equations for SNN algorithms usually contain variables with one or more e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sup> components. SpiNNaker is a digital neuromorphic chip that has so far been using pre-calculated look-up tables exponential function. However this approach limited because the...