1,931 research outputs found
Realising the open virtual commissioning of modular automation systems
To address the challenges in the automotive industry posed by the need to rapidly manufacture more
product variants, and the resultant need for more adaptable production systems, radical changes are
now required in the way in which such systems are developed and implemented. In this context, two
enabling approaches for achieving more agile manufacturing, namely modular automation systems
and virtual commissioning, are briefly reviewed in this contribution. Ongoing research conducted at
Loughborough University which aims to provide a modular approach to automation systems design
coupled with a virtual engineering toolset for the (re)configuration of such manufacturing
automation systems is reported. The problems faced in the virtual commissioning of modular
automation systems are outlined. AutomationML - an emerging neutral data format which has
potential to address integration problems is discussed. The paper proposes and illustrates a
collaborative framework in which AutomationML is adopted for the data exchange and data
representation of related models to enable efficient open virtual prototype construction and virtual
commissioning of modular automation systems. A case study is provided to show how to create the
data model based on AutomationML for describing a modular automation system
Smart technologies for effective reconfiguration: the FASTER approach
Current and future computing systems increasingly require that their functionality stays flexible after the system is operational, in order to cope with changing user requirements and improvements in system features, i.e. changing protocols and data-coding standards, evolving demands for support of different user applications, and newly emerging applications in communication, computing and consumer electronics. Therefore, extending the functionality and the lifetime of products requires the addition of new functionality to track and satisfy the customers needs and market and technology trends. Many contemporary products along with the software part incorporate hardware accelerators for reasons of performance and power efficiency. While adaptivity of software is straightforward, adaptation of the hardware to changing requirements constitutes a challenging problem requiring delicate solutions. The FASTER (Facilitating Analysis and Synthesis Technologies for Effective Reconfiguration) project aims at introducing a complete methodology to allow designers to easily implement a system specification on a platform which includes a general purpose processor combined with multiple accelerators running on an FPGA, taking as input a high-level description and fully exploiting, both at design time and at run time, the capabilities of partial dynamic reconfiguration. The goal is that for selected application domains, the FASTER toolchain will be able to reduce the design and verification time of complex reconfigurable systems providing additional novel verification features that are not available in existing tool flows
Approximate FPGA-based LSTMs under Computation Time Constraints
Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM)
networks have demonstrated state-of-the-art accuracy in several emerging
Artificial Intelligence tasks. However, the models are becoming increasingly
demanding in terms of computational and memory load. Emerging latency-sensitive
applications including mobile robots and autonomous vehicles often operate
under stringent computation time constraints. In this paper, we address the
challenge of deploying computationally demanding LSTMs at a constrained time
budget by introducing an approximate computing scheme that combines iterative
low-rank compression and pruning, along with a novel FPGA-based LSTM
architecture. Combined in an end-to-end framework, the approximation method's
parameters are optimised and the architecture is configured to address the
problem of high-performance LSTM execution in time-constrained applications.
Quantitative evaluation on a real-life image captioning application indicates
that the proposed methods required up to 6.5x less time to achieve the same
application-level accuracy compared to a baseline method, while achieving an
average of 25x higher accuracy under the same computation time constraints.Comment: Accepted at the 14th International Symposium in Applied
Reconfigurable Computing (ARC) 201
Self-Optimising Reactive Extractions: Towards the Efficient Development of Multi-Step Continuous Flow Processes
Downstream purification of products and intermediates is essential for the development of continuous flow processes. Described herein, is a study on the use of a modular and reconfigurable continuous flow platform for the self-optimisation of reactive extractions and multi-step reaction-extraction processes. The selective extraction of one amine from a mixture of two similar amines was achieved with an optimum separation of 90%, and in this case, the black-box optimisation approach was superior to global polynomial modelling. Furthermore, this methodology was utilised to simultaneously optimise the continuous flow synthesis and work-up of N-benzyl-α-methylbenzylamine with respect to four variables, resulting in a significantly improved purity
Optimising runtime reconfigurable designs for high performance applications
This thesis proposes novel optimisations for high performance runtime reconfigurable designs.
For a reconfigurable design,
the proposed approach investigates idle resources introduced by static design approaches,
and exploits runtime reconfiguration to eliminate the inefficient resources.
The approach covers the circuit level, the function level, and the system level.
At the circuit level, a method is proposed for tuning reconfigurable designs with two analytical models:
a resource model for computational and memory resources and memory bandwidth,
and a performance model for estimating execution time.
This method is applied to tuning implementations of finite-difference algorithms,
optimising arithmetic operators and memory bandwidth based on algorithmic parameters,
and eliminating idle resources by runtime reconfiguration.
At the function level, a method is proposed to automatically identify and exploit runtime
reconfiguration opportunities while optimising resource utilisation.
The method is based on Reconfiguration Data Flow Graph,
a new hierarchical graph structure enabling runtime reconfigurable designs to be synthesised in three steps:
function analysis, configuration organisation, and runtime solution generation.
At the system level, a method is proposed for optimising reconfigurable designs by dynamically adapting the designs to available runtime resources in a reconfigurable system. This method includes two steps: compile-time optimisation and runtime scaling, which enable efficient workload distribution, asynchronous communication scheduling, and domain-specific optimisations. It can be used in developing effective servers for high performance applications.Open Acces
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
In the past decade, Convolutional Neural Networks (CNNs) have demonstrated
state-of-the-art performance in various Artificial Intelligence tasks. To
accelerate the experimentation and development of CNNs, several software
frameworks have been released, primarily targeting power-hungry CPUs and GPUs.
In this context, reconfigurable hardware in the form of FPGAs constitutes a
potential alternative platform that can be integrated in the existing deep
learning ecosystem to provide a tunable balance between performance, power
consumption and programmability. In this paper, a survey of the existing
CNN-to-FPGA toolflows is presented, comprising a comparative study of their key
characteristics which include the supported applications, architectural
choices, design space exploration methods and achieved performance. Moreover,
major challenges and objectives introduced by the latest trends in CNN
algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and
in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal,
201
High performance WR-1.5 corrugated horn based on stacked rings
We present the development and characterisation of a high frequency (500-750
GHz) corrugated horn based on stacked rings. A previous horn design, based on a
Winston profile, has been adapted for the purpose of this manufacturing process
without noticeable RF degradation. A subset of experimental results obtained
using a vector network analyser are presented and compared to the predicted
performance. These first results demonstrate that this technology is suitable
for most commercial applications and also astronomical receivers in need of
horn arrays at high frequencies.Comment: 9 page
Common Representation of Information Flows for Dynamic Coalitions
We propose a formal foundation for reasoning about access control policies
within a Dynamic Coalition, defining an abstraction over existing access
control models and providing mechanisms for translation of those models into
information-flow domain. The abstracted information-flow domain model, called a
Common Representation, can then be used for defining a way to control the
evolution of Dynamic Coalitions with respect to information flow
FPGA Architecture Optimization Using Geometric Programming
Volume 4 No 13 of the periodical Progression. Published November, February, May and August by The Radiant Healing Centre. SPCL PER BT 732 P76 V.1,1932-V.5,193
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