Anders Ahlander

Time-deterministic WDM star network for massively parallel computing in radar systems

In massively parallel computer systems for embedded real-time applications there are normally very high bandwidth demands on the interconnection network. Other important properties are time-deterministic latency and services to guarantee that deadlines are met. In this paper we analyze how these properties vary with the design parameters for a passive optical star network, specifically when used in a massively parallel radar signal processing system. The aggregated bandwidth and computational power of the radar system are approximately 45 Gb/s and 100 GOPS, respectively. The analysis is focused on the medium access control protocol, called TD-TWDMA, for the time and wavelength multiplexed network. It is concluded that the proposed network is very well suited to this kind of signal-processing applications. We also present a new distributed slot-allocation algorithm with real-time properties.

Fiber-ribbon pipeline ring network for high-performance distributed computing systems

In this paper, we propose a high-bandwidth ring network built up with fiber-ribbon point-to-point links. The network has support for both packet switched and circuit switched traffic. Very high throughputs can be achieved in the network due to pipelining, i.e., several packets can be traveling through the network simultaneously but in different segments of the ring. The network can be built today using fiber-optic off-the-shelf components. The increasingly good price/performance ratio for fiber-ribbon links indicates a great success potential for the proposed kind of networks. We also present a massively parallel radar signal processing system with exceptionally high demands on the communication network. An aggregated throughput of tens of Gb/s is needed in this application, and this is achieved with the proposed network.

Autofocus in fast factorized backprojection for processing of SAR images when geometry parameters are unknown

This paper introduces a new autofocus method for high-resolution SAR systems. The new method relies on varying antenna path parameters, i.e. the cause of the focusing problem. The variation and determination of antenna path parameters is computed and structured by incorporating the method into the framework of fast factorized backprojection, and thus also blending deterministic focus and autofocus into one method. The new autofocus has been tested with wavelength-resolution SAR data with good results.

Energy-Efficient Synthetic-Aperture Radar Processing on a Manycore Architecture

The next generation radar systems have high performance demands on the signal processing chain. Examples include the advanced image creating sensor systems in which complex calculations are to be performed on huge sets of data in real time. Many core architectures are gaining attention as a means to overcome the computational requirements of the complex radar signal processing by exploiting massive parallelism inherent in the algorithms in an energy efficient manner. In this paper, we evaluate a many core architecture, namely a 16-core Epiphany processor, by implementing two significantly large case studies, viz. an auto focus criterion calculation and the fast factorized back-projection algorithm, both key components in modern synthetic aperture radar systems. The implementation results from the two case studies are compared on the basis of achieved performance and programmability. One of the Epiphany implementations demonstrates the usefulness of the architecture for the streaming based algorithm (the auto focus criterion calculation) by achieving a speedup of 8.9x over a sequential implementation on a state-of-the-art general-purpose processor of a later silicon technology generation and operating at a 2.7x higher clock speed. On the other case study, a highly memory-intensive algorithm (fast factorized back projection), the Epiphany architecture shows a speedup of 4.25x. For embedded signal processing, low power dissipation is equally important as computational performance. In our case studies, the Epiphany implementations of the two algorithms are, respectively, 78x and 38x more energy efficient.

Real-time radar signal processing on Massively Parallel Processor Arrays

The next generation radar systems have high performance demands on the signal processing chain. Among these are advanced image creating sensor systems in which complex calculations are to be performed on huge sets of data in realtime. Massively Parallel Processor Arrays (MPPAs) are gaining attention to cope with the computational requirements of complex radar signal processing by exploiting the massive parallelism inherent in the algorithms in an energy efficient manner. In this paper, we evaluate two such massively parallel architectures, namely, Ambric and Epiphany, by implementing a significantly large case study of autofocus criterion calculation, which is a key component in future synthetic aperture radar systems. The implementation results from the two case studies are compared on the basis of achieved performance, energy efficiency, and programmability.

Programming Real-Time Autofocus on a Massively Parallel Reconfigurable Architecture Using Occam-pi

Recently we proposed occam-pi as a high-level language for programming massively parallel reconfigurable architectures. The design of occam-pi incorporates ideas from CSP and pi-calculus to facilitate expressing parallelism and reconfigurability. The feasability of this approach was illustrated by building three occam-pi implementations of DCT executing on an Ambric. However, because DCT is a simple and well-studied algorithm it remained uncertain whether occam-pi would also be effective for programming novel, more complex algorithms. In this paper, we demonstrate the applicability of occam-pi for expressing various degrees of parallelism by implementing a significantly large case-study of focus criterion calculation in an auto focus algorithm on the Ambric architecture. Auto focus is a key component of synthetic aperture radar systems. Two implementations of focus criterion calculation were developed and evaluated on the basis of performance. The comparison of the performance results with a single threaded software implementation of the same algorithm show that the throughput of the two implementations are 11x and 23x higher than the sequential implementation despite a much lower (9x) clock frequency. The two designs are, respectively, 29x and 40x more energy efficient.

Radar signal processing using pipelined optical hypercube interconnects

In this paper, we consider the mapping of two radar algorithms on a new scalable hardware architecture. The architecture consists of several computational modules that work independently and send data simultaneously in order to achieve high throughput. Each computational module is composed of multiple processors connected in a hypercube topology to meet scalability and high bisection bandwidth requirements. Free-space optical interconnects and planar packaging technology make it possible to transform the hypercubes into planes. Optical fan-out reduces the number of optical transmitters and thus the hardware cost. Two example systems are analyzed and mapped onto the architecture. One 64-channel airborne radar system with a sustained computational load of more than 1.6 TFLOPS, and one ground-based 128-channel radar system with extreme inter-processor communication demands.

A running leap for embedded signal processing to future parallel platforms

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.