Turku Centre for Computer Science

About: Turku Centre for Computer Science is a facility organization based out in Turku, Finland. It is known for research contribution in the topics: Decidability & Word (group theory). The organization has 382 authors who have published 1027 publications receiving 19560 citations.

Model-Based Testing Using Scenarios and Event-B Refinements

Abstract: In this paper, we present a model-based testing approach based on user provided testing scenarios. In this approach, when a software model is refined to add or modify features, the corresponding testing scenarios are automatically refined to incorporate these changes. The test cases, to be applied on the system under test, are generated from these scenarios. We use the Event-B formalism for software models, while user scenarios are represented as Communicating Sequential Process (CSP) expressions. The presented case study demonstrates how our approach can be used to test different features of a system such as incorporated fault-tolerance mechanisms.

Inheritance Using Contracts & Object Composition

Abstract: Normal class-based code inheritance across component boundaries creates a dependency between the involved components. To avoid this, a specification of the inherited class must be part of the respective component's contract and the inheriting class must be specified with reference to this specification only. With this, inheritance can be replaced by object composition without sacrificing the possibility of static analysis, yet being more flexible

NeuroCGRA: A CGRA with support for neural networks

Abstract: Today, Coarse Grained Reconfigurable Architectures (CGRAs) are becoming an increasingly popular implementation platform. In real world applications, the CGRAs are required to simultaneously host processing (e.g. Audio/video acquisition) and estimation (e.g. audio/video/image recognition) tasks. For estimation problems, neural networks, promise a higher efficiency than conventional processing. However, most of the existing CGRAs provide no support for neural networks. To realize realize both neural networks and conventional processing on the same platform, this paper presents NeuroCGRA. NeuroCGRA allows the processing elements and the network to dynamically morph into either conventional CGRA or a neural network, depending on the hosted application. We have chosen the DRRA as a vehicle to study the feasibility and overheads of our approach. Synthesis results reveal that the proposed enhancements incur negligible overheads (4.4% area and 9.1% power) compared to the original DRRA cell.