This paper highlights the collaboration between industry and academia in research. It describes more than two decades of intensive development and research of new hardware and software platforms to support innovative, high-performance sensor systems with extremely high demands on embedded signal processing capability. The joint research can be seen as the run before a necessary jump to a new kind of computational platform based on parallelism. The collaboration has had several phases, starting with a focus on hardware, then on efficiency, later on software development, and finally on taking the jump and understanding the expected future. In the first part of the paper, these phases and their respective challenges and results are described. Then, in the second part, we reflect upon the motivation for collaboration between company and university, the roles of the partners, the experiences gained and the long-term effects on both sides. Copyright © 2014 ACM.

The programming complexity of increasingly parallel processors calls for new tools to assist programmers in utilising the parallel hardware resources. In this paper we present a set of models that we have developed to form part of a tool which is intended for iteratively tuning the mapping of dataflow graphs onto manycores. One of the models is used for capturing the essentials of manycores that are identified as suitable for signal processing and which we use as target architectures. Another model is the intermediate representation in the form of a timed configuration graph, describing the mapping of a dataflow graph onto a machine model. Moreover, this IR can be used for performance evaluation using abstract interpretation. We demonstrate how the models can be configured and applied in order to map applications on the Raw processor. Furthermore, we report promising results on the accuracy of performance predictions produced by our tool. It is also demonstrated that the tool can be used to rank different mappings with respect to optimisation on throughput and end-to-end latency.

Advanced digital signal processing systems require specialized high-performance embedded computer architectures. The term high-performance translates to large amounts of data and computations per time unit. The term embedded further implies requirements on physical size and power efficiency. Thus the requirements are of both functional and non-functional nature. This thesis addresses the development of high-performance digital signal processing systems relying on manycore technology. We propose building two-level hierarchical computer architectures for this domain of applications. Further, we outline a tool flow based on methods and analysis techniques for automated, multi-objective mapping of such applications on distributed memory manycore processors. In particular, the focus is put on how to provide a means for tunable strategies for mapping of task graphs on array structured distributed memory manycores, with respect to given application constraints. We argue for code mapping strategies based on predicted execution performance, which can be used in an auto-tuning feedback loop or to guide manual tuning directed by the programmer. Automated parallelization, optimisation and mapping to a manycore processor benefits from the use of a concurrent programming model as the starting point. Such a model allows the programmer to express different types and granularities of parallelism as well as computation characteristics of importance in the addressed class of applications. The programming model should also abstract away machine dependent hardware details. The analytical study of WCDMA baseband processing in radio base stations, presented in this thesis, suggests dataflow models as a good match to the characteristics of the application and as execution model abstracting computations on a manycore. Construction of portable tools further requires a manycore machine model and an intermediate representation. The models are needed in order to decouple algorithms, used to transform and map application software, from hardware. We propose a manycore machine model that captures common hardware resources, as well as resource dependent performance metrics for parallel computation and communication. Further, we have developed a multifunctional intermediate representation, which can be used as source for code generation and for dynamic execution analysis. Finally, we demonstrate how we can dynamically analyse execution using abstract interpretation on the intermediate representation. It is shown that the performance predictions can be used to accurately rank different mappings by best throughput or shortest end-to-end computation latency.

The programming complexity of increasingly parallel processors calls for new tools that assist programmers in utilising the parallel hardware resources. In this paper we present a set of models that we have developed as part of a tool for mapping dataflow graphs onto manycores. One of the models captures the essentials of manycores identified as suitable for signal processing, and which we use as tar- get for our algorithms. As an intermediate representation we introduce timed configuration graphs, which describe the mapping of a model of an application onto a machine model. Moreover, we show how a timed configuration graph by very simple means can be evaluated using an abstract interpretation to obtain performance feedback. This infor- mation can be used by our tool and by the programmer in order to discover improved mappings.

The programming complexity of increasingly parallel processors calls for new tools that assist programmers in utilising the parallel hardware resources. In this paper we present a set of models that we have developed as part of a tool for mapping dataflow graphs onto manycores. One of the models captures the essentials of manycores identified as suitable for signal processing, and which we use as target for our algorithms. As an intermediate representation we introduce timed configuration graphs, which describe the mapping of a model of an application onto a machine model. Moreover, we show how a timed configuration graph by very simple means can be evaluated using an abstract interpretation to obtain performance feedback. This information can be used by our tool and by the programmer in order to discover improved mappings.

IEEE 802.15.4 is a new enabling technology for low data rate wireless personal networks. This standard was not specifically designed for wireless sensor networks, but it has shown to be a good match with necessary requirements on low data rate, low power consumption and low cost. Unlike the former 802.11 standard, the MAC protocol specified in IEEE 802.15.4 can operate in two different modes: beacon-enabled mode or non-beacon enable mode. In beacon-enabled mode, nodes can exclusively allocate a number of guaranteed time slots, similar to a resource reservation scheme. Hence, the IEEE 802.15.4 MAC protocol have sufficient capabilities for supporting real-time communication. This paper presents the key features of IEEE 802.15.4 which makes it an attractive standard to use for real-time wireless sensor networks. Two real-time protocols extending the IEEE 802.15.4 standard are reviewed. The purpose of this paper is to present the state of the art on real-time support over IEEE 802.15.4 for wireless sensor networks and to discuss the possibilities on improvements on both the standard and the real-time protocols extending the standard.