Factories of the Future

Engineering; Industrial engineering; Production engineerin

Variability-Aware Circuit Performance Optimisation Through Digital Reconfiguration

This thesis proposes optimisation methods for improving the performance of circuits imple- mented on a custom reconfigurable hardware platform with knowledge of intrinsic variations, through the use of digital reconfiguration. With the continuing trend of transistor shrinking, stochastic variations become first order effects, posing a significant challenge for device reliability. Traditional device models tend to be too conservative, as the margins are greatly increased to account for these variations. Variation-aware optimisation methods are then required to reduce the performance spread caused by these substrate variations. The Programmable Analogue and Digital Array (PAnDA) is a reconfigurable hardware plat- form which combines the traditional architecture of a Field Programmable Gate Array (FPGA) with the concept of configurable transistor widths, and is used in this thesis as a platform on which variability-aware circuits can be implemented. A model of the PAnDA architecture is designed to allow for rapid prototyping of devices, making the study of the effects of intrinsic variability on circuit performance – which re- quires expensive statistical simulations – feasible. This is achieved by means of importing statistically-enhanced transistor performance data from RandomSPICE simulations into a model of the PAnDA architecture implemented in hardware. Digital reconfiguration is then used to explore the hardware resources available for performance optimisation. A bio-inspired optimisation algorithm is used to explore the large solution space more efficiently. Results from test circuits suggest that variation-aware optimisation can provide a significant reduction in the spread of the distribution of performance across various instances of circuits, as well as an increase in performance for each. Even if transistor geometry flexibility is not available, as is the case of traditional architectures, it is still possible to make use of the substrate variations to reduce spread and increase performance by means of function relocation

GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

The Second Spaceborne Imaging Radar Symposium

Summaries of the papers presented at the Second Spaceborne Imaging Radar Symposium are presented. The purpose of the symposium was to present an overwiew of recent developments in the different scientific and technological fields related to spaceborne imaging radars and to present future international plans