Software-Defined Radio Demonstrators: An Example and Future Trends

Software-defined radio requires the combination of software-based signal processing and the enabling hardware components. In this paper, we present an overview of the criteria for such platforms and the current state of development and future trends in this area. This paper will also provide details of a high-performance flexible radio platform called the maynooth adaptable radio system (MARS) that was developed to explore the use of software-defined radio concepts in the provision of infrastructure elements in a telecommunications application, such as mobile phone basestations or multimedia broadcasters

Video Processing Acceleration using Reconfigurable Logic and Graphics Processors

A vexing question is `which architecture will prevail as the core feature of the next state of the art video processing system?' This thesis examines the substitutive and collaborative use of the two alternatives of the reconfigurable logic and graphics processor architectures. A structured approach to executing architecture comparison is presented - this includes a proposed `Three Axes of Algorithm Characterisation' scheme and a formulation of perfor- mance drivers. The approach is an appealing platform for clearly defining the problem, assumptions and results of a comparison. In this work it is used to resolve the advanta- geous factors of the graphics processor and reconfigurable logic for video processing, and the conditions determining which one is superior. The comparison results prompt the exploration of the customisable options for the graphics processor architecture. To clearly define the architectural design space, the graphics processor is first identifed as part of a wider scope of homogeneous multi-processing element (HoMPE) architectures. A novel exploration tool is described which is suited to the investigation of the customisable op- tions of HoMPE architectures. The tool adopts a systematic exploration approach and a high-level parameterisable system model, and is used to explore pre- and post-fabrication customisable options for the graphics processor. A positive result of the exploration is the proposal of a reconfigurable engine for data access (REDA) to optimise graphics processor performance for video processing-specific memory access patterns. REDA demonstrates the viability of the use of reconfigurable logic as collaborative `glue logic' in the graphics processor architecture

An OpenCL software compilation framework targeting an SoC-FPGA VLIW chip multiprocessor

Modern systems-on-chip augment their baseline CPU with coprocessors and accelerators to increase overall computational capability and power efficiency, and thus have evolved into heterogeneous multi-core systems. Several languages have been developed to enable this paradigm shift, including CUDA and OpenCL. This paper discusses a unified compilation environment to enable heterogeneous system design through the use of OpenCL and a highly configurable VLIW Chip Multiprocessor architecture known as the LE1. An LLVM compilation framework was researched and a prototype developed to enable the execution of OpenCL applications on a number of hardware configurations of the LE1 CMP. The presented OpenCL framework fully automates the compilation flow and supports work-item coalescing which better maps onto the ILP processor cores of the LE1 architecture. This paper discusses in detail both the software stack and target hardware architecture and evaluates the scalability of the proposed framework by running 12 industry-standard OpenCL benchmarks drawn from the AMD SDK and the Rodinia suites. The benchmarks are executed on 40 LE1 configurations with 10 implemented on an SoC-FPGA and the remaining on a cycle-accurate simulator. Across 12 OpenCL benchmarks results demonstrate near-linear wall-clock performance improvement of 1.8x (using 2 dual-issue cores), up to 5.2x (using 8 dual-issue cores) and on one case, super-linear improvement of 8.4x (FixOffset kernel, 8 dual-issue cores). The number of OpenCL benchmarks evaluated makes this study one of the most complete in the literature

Simplified vector-thread architectures for flexible and efficient data-parallel accelerators

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 165-170).This thesis explores a new approach to building data-parallel accelerators that is based on simplifying the instruction set, microarchitecture, and programming methodology for a vector-thread architecture. The thesis begins by categorizing regular and irregular data-level parallelism (DLP), before presenting several architectural design patterns for data-parallel accelerators including the multiple-instruction multiple-data (MIMD) pattern, the vector single-instruction multiple-data (vector-SIMD) pattern, the single-instruction multiple-thread (SIMT) pattern, and the vector-thread (VT) pattern. Our recently proposed VT pattern includes many control threads that each manage their own array of microthreads. The control thread uses vector memory instructions to efficiently move data and vector fetch instructions to broadcast scalar instructions to all microthreads. These vector mechanisms are complemented by the ability for each microthread to direct its own control flow. In this thesis, I introduce various techniques for building simplified instances of the VT pattern. I propose unifying the VT control-thread and microthread scalar instruction sets to simplify the microarchitecture and programming methodology. I propose a new single-lane VT microarchitecture based on minimal changes to the vector-SIMD pattern.(cont.) Single-lane cores are simpler to implement than multi-lane cores and can achieve similar energy efficiency. This new microarchitecture uses control processor embedding to mitigate the area overhead of single-lane cores, and uses vector fragments to more efficiently handle both regular and irregular DLP as compared to previous VT architectures. I also propose an explicitly data-parallel VT programming methodology that is based on a slightly modified scalar compiler. This methodology is easier to use than assembly programming, yet simpler to implement than an automatically vectorizing compiler. To evaluate these ideas, we have begun implementing the Maven data-parallel accelerator. This thesis compares a simplified Maven VT core to MIMD, vector-SIMD, and SIMT cores. We have implemented these cores with an ASIC methodology, and I use the resulting gate-level models to evaluate the area, performance, and energy of several compiled microbenchmarks. This work is the first detailed quantitative comparison of the VT pattern to other patterns. My results suggest that future data-parallel accelerators based on simplified VT architectures should be able to combine the energy efficiency of vector-SIMD accelerators with the flexibility of MIMD accelerators.by Christopher Francis Batten.Ph.D

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems