What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Computer network
His scientific interests lie mostly in Spiking neural network, Computer architecture, Asynchronous communication, Massively parallel and SpiNNaker.
Spiking neural network is the subject of his research, which falls under Artificial neural network.
He interconnects C-element and Chip in the investigation of issues within Computer architecture.
His Asynchronous communication study combines topics from a wide range of disciplines, such as Instruction set, Embedded system, Network on a chip and Router.
The study incorporates disciplines such as Multiprocessing, Adaptation, Leaky integrator and Artificial intelligence in addition to Massively parallel.
His work carried out in the field of SpiNNaker brings together such families of science as Power efficient, MNIST database and Simulation.
His most cited work include:
- Principles of Asynchronous Circuit Design: A Systems Perspective (641 citations)
- The SpiNNaker Project (619 citations)
- Principles of Asynchronous Circuit Design (463 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Spiking neural network, Neuromorphic engineering, Asynchronous communication, Artificial intelligence and Artificial neural network.
In his study, Distributed computing is strongly linked to Massively parallel, which falls under the umbrella field of Spiking neural network.
His Neuromorphic engineering research is multidisciplinary, incorporating elements of Event, Software, Computer hardware and Chip.
His Asynchronous communication research includes themes of ARM architecture, Embedded system, Electronic circuit and Multiprocessing.
His Artificial neural network research integrates issues from Model of computation, Spike, Neuron, Real-time computing and Algorithm.
His study in SpiNNaker is interdisciplinary in nature, drawing from both Synapse, Computer architecture and Parallel computing.
He most often published in these fields:
- Spiking neural network (23.51%)
- Neuromorphic engineering (21.75%)
- Asynchronous communication (21.75%)
What were the highlights of his more recent work (between 2018-2021)?
- Neuromorphic engineering (21.75%)
- Spiking neural network (23.51%)
- SpiNNaker (19.30%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Neuromorphic engineering, Spiking neural network, SpiNNaker, Computer architecture and Artificial neural network.
His research integrates issues of Scalability, Overhead, Computer hardware, Chip and Cognitive science in his study of Neuromorphic engineering.
The Spiking neural network study combines topics in areas such as Field-programmable gate array, Massively parallel, Parallel computing and Robustness.
The concepts of his SpiNNaker study are interwoven with issues in Embedded system, Asynchronous communication, Task, Software architecture and Robot.
His Computer architecture research includes elements of Context, Image sensor and Benchmark.
His Artificial neural network study is associated with Artificial intelligence.
Between 2018 and 2021, his most popular works were:
- Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware. (14 citations)
- Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype (13 citations)
- Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations (12 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Computer network
Steve Furber mostly deals with Neuromorphic engineering, Spiking neural network, Computation, SpiNNaker and Artificial intelligence.
His Neuromorphic engineering research incorporates themes from Scalability, Computer hardware, Frequency scaling and MPSoC.
Steve Furber studied Scalability and Massively parallel that intersect with Computer architecture.
His studies in Spiking neural network integrate themes in fields like Embedding, Supervised learning, Unsupervised learning and Robustness.
His SpiNNaker study incorporates themes from Structural plasticity, Distributed computing, Neuromorphic hardware, Parallel computing and Scheme.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and GRASP.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
