Power Aware Learning for Class AB Analogue VLSI Neural Network

Recent research into Artificial Neural Networks (ANN) has highlighted the potential of using compact analogue ANN hardware cores in embedded mobile devices, where power consumption of ANN hardware is a very significant implementation issue. This paper proposes a learning mechanism suitable for low-power class AB type analogue ANN that not only tunes the network to obtain minimum error, but also adaptively learns to reduce power consumption. Our experiments show substantial reductions in the power budget (30% to 50%) for a variety of example networks as a result of our power-aware learning

Power scalable implementation of artificial neural networks

As the use of Artificial Neural Network (ANN) in mobile embedded devices gets more pervasive, power consumption of ANN hardware is becoming a major limiting factor. Although considerable research efforts are now directed towards low-power implementations of ANN, the issue of dynamic power scalability of the implemented design has been largely overlooked. In this paper, we discuss the motivation and basic principles for implementing power scaling in ANN Hardware. With the help of a simple example, we demonstrate how power scaling can be achieved with dynamic pruning techniques

Auction Design for Standard Offer Service

During the transition to a competitive electricity market, when a consumer does not select an electricity provider, who provides service to the customer and at what price? An auction for this "standard offer service" is a market-based way to assign the service responsibility and to determine its price. We explore the design issues in establishing rules for such an auction.Auctions; Pricing; Electric Utilities

Yield Model Characterization For Analog Integrated Circuit Using Pareto-Optimal Surface

A novel technique is proposed in this paper that achieves a yield optimized design from a set of optimal performance points on the Pareto front. Trade-offs among performance functions are explored through multi-objective optimization and Monte Carlo simulation is used to find the design point producing the best overall yield. One advantage of the approach presented is a reduction in the computational cost normally associated with Monte Carlo simulation. The technique offers a yield optimized robust circuit design solution with transistor level accuracy. An example using an OTA is presented to demonstrate the effectiveness of the work

Behavioural simulation of biological neuron systems using VHDL and VHDL-AMS

The investigation of neuron structures is an incredibly difficult and complex task that yields relatively low rewards in terms of information from biological forms (either animals or tissue). The structures and connectivity of even the simplest invertebrates are almost impossible to establish with standard laboratory techniques, and even when this is possible it is generally time consuming, complex and expensive. Recent work has shown how a simplified behavioural approach to modelling neurons can allow “virtual” experiments to be carried out that map the behaviour of a simulated structure onto a hypothetical biological one, with correlation of behaviour rather than underlying connectivity. The problems with such approaches are numerous. The first is the difficulty of simulating realistic aggregates efficiently, the second is making sense of the results and finally, it would be helpful to have an implementation that could be synthesised to hardware for acceleration. In this paper we present a VHDL implementation of Neuron models that allow large aggregates to be simulated. The models are demonstrated using a system level VHDL and VHDL-AMS model of the C. Elegans locomotory system

An Intelligent Fuse-box for use with Renewable Energy Sources integrated within a Domestic Environment

This paper outlines a proposal for an intelligent fuse-box that can replace existing fuse-boxes in a domestic context such that a number of renewable energy sources can easily be integrated into the domestic power supply network, without the necessity for complex islanding and network protection. The approach allows intelligent control of both the generation of power and its supply to single or groups of electrical appliances. Energy storage can be implemented in such a scheme to even out the power supplied and simplify the control scheme required, and environmental monitoring and load analysis can help in automatically controlling the supply and demand profiles for optimum electrical and economic efficiency. Simulations of typical scenarios are carried out to illustrate the concept in operation

A new species of Rhodomyrtus (Myrtaceae) with brochidodromous venation from north-eastern Queensland, Australia.

Rhodomyrtus verecunda A.J.Ford & Peter G. Wilson from north-east Queensland, Australia, is formally described and illustrated. Notes on habitat, distribution and conservation status are provided, as are details of how it can be distinguished from R. macrocarpa, the only other species of the genus in the region that lacks acrodromous venation. A revised key to the species of Rhodomyrtus in Australia is also presented