Advancing interconnect density for spiking neural network hardware implementations using traffic-aware adaptive network-on-chip routers

Carrillo, Snaider; Harkin, Jim; McDaid, Liam; Pande, Sandeep; Cawley, Seamus; McGinley, Brian; Morgan, Frank

doi:10.1016/j.neunet.2012.04.004

Cited by 61 publications

(88 citation statements)

References 29 publications

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“…It can be noticed that the traffic status weight of each port is calculated in real-time. This allows the priorities of all ports are updated in real-time to access to the output channel [4].…”

Section: Efficient Routing Architecture For the Inter-neuron Connectionmentioning

confidence: 99%

“…For the inter-neuron connections, the networks-on-chip (NoC) has been mostly used as an efficient SNN interconnect strategy. For example, a routing architecture based on two-dimensional (2D) mesh topology was proposed in the approach of [4]. The FACETS in the approach of [5] was based on a 2D torus which provided the connections of several FACETS wafers.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Hardware Architecture for Multilayer Spiking Neural Networks

Luo

Wan

Liu

et al. 2017

Neural Information Processing

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Abstract. Spiking Neural Network (SNN) is the most recent computational model that can emulate the behaviors of biological neuron system. This paper highlights and discusses an efficient hardware architecture for the hardware SNNs, which includes a layer-level tile architecture (LTA) for the neurons and synapses, and a novel routing architecture (NRA) for the interconnections between the neuron nodes. In addition, a visualization performance monitoring platform is designed, which is used as functional verification and performance monitoring for the SNN hardware system. Experimental results demonstrate that the proposed architecture is feasible and capable of scaling to large hardware multilayer SNNs.

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Section: Efficient Routing Architecture For the Inter-neuron Connectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Hardware Architecture for Multilayer Spiking Neural Networks

Luo

Wan

Liu

et al. 2017

Neural Information Processing

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show abstract

“…Based on these, ANNs offer high prediction capabilities. ANNs also have been successfully applied in various real life scenarios which include learning systems [13], neuroscience [14], and engineering [15]. Through these, it is believed that ANNs are a powerful tool which has the ability to make predictions based on complex relations of the input and output data.…”

Section: Ann and Prediction Related Workmentioning

confidence: 99%

A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems

Savva

Theocharides

Nicopoulos

2017

Journal of Electrical and Computer Engineering

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This work presents a design exploration framework for developing a high level Artificial Neural Network (ANN) for fault detection in hardware systems. ANNs can be used for fault detection purposes since they have excellent characteristics such as generalization capability, robustness, and fault tolerance. Designing an ANN in order to be used for fault detection purposes includes different parameters. Through this work, those parameters are presented and analyzed based on simulations. Moreover, after the development of the ANN, in order to evaluate it, a case study scenario based on Networks on Chip is used for detection of interrouter link faults. Simulation results with various synthetic traffic models show that the proposed work can detect up to 96-99% of interrouter link faults with a delay less than 60 cycles. Added to this, the size of the ANN is kept relatively small and they can be implemented in hardware easily. Synthesis results indicate an estimated amount of 0.0523 mW power consumption per neuron for the implemented ANN when computing a complete cycle.

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“…ANN has been successfully applied in various disciplines, including neuroscience [19], mathematical and computational analysis [20], learning systems [21], engineering design and application [22][23][24] and chemical and environmental engineering [25][26][27][28]. In this paper, the potential of ANN modelling techniques in identifying sustainable future generation biodiesel feedstock are identified based on the most recent literature.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

et al. 2013

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Abstract:Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer term due to the rising food prices and the concerns about automobile engine durability. Therefore, there is an urgent need for researchers to identify and develop sustainable biodiesel feedstocks which overcome the disadvantages of current ones. On the other hand, artificial neural network (ANN) modeling has been successfully used in recent years to gain new knowledge in various disciplines. The main goal of this article is to review recent literatures and assess the state of the art on the use of ANN as a modeling tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production processes, chemical compositions, standards, physio-chemical properties and in-use performance are discussed. Limitations of current biodiesel feedstocks over future generation biodiesel feedstock have been identified. The application of ANN in modeling key biodiesel quality parameters and combustion performance in automobile engines is also discussed. This review has determined that ANN modeling has a high potential to contribute to the development of renewable energy systems by accelerating biodiesel research.

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Advancing interconnect density for spiking neural network hardware implementations using traffic-aware adaptive network-on-chip routers

Cited by 61 publications

References 29 publications

An Efficient Hardware Architecture for Multilayer Spiking Neural Networks

An Efficient Hardware Architecture for Multilayer Spiking Neural Networks

A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems

The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

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