Implementation of Memory Centric Scheduling for COTS Multi-Core Real-Time Systems

The demands for high performance computing with a low cost and low power consumption are driving a transition towards multi-core processors in many consumer and industrial applications. However, the adoption of multi-core processors in the domain of real-time systems faces a series of challenges that has been the focus of great research intensity during the last decade. These challenges arise in great part from the non real-time nature of the hardware arbiters that schedule the access to shared resources, such as the main memory. One solution proposed in the literature is called Memory Centric Scheduling, which defines a separate software scheduler for the sections of the tasks that will access the main memory, hence circumventing the low level unpredictable hardware arbiters. Several Memory Centric schedulers and associated theoretical analyses have been proposed, but as far as we know, no actual implementation of the required OS-level underpinnings to support dynamic event-driven Memory Centric Scheduling has been presented before. In this paper we aim to fill this gap, targeting cache based COTS multi-core systems. We will confirm via measurements the main theoretical benefits of Memory Centric Scheduling (e.g. task isolation). Furthermore, we will describe an effective schedulability analysis using concepts from distributed systems

Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations

We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN). Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straight-through gradient estimator is used to model the leaky integrate-fire spiking neuron, overcoming the difficulty in training SNNs using back propagation. Two SNN training algorithms are proposed: (1) SNN with discontinuous integration, which is suitable for rate-coded input spikes, and (2) SNN with continuous integration, which is more general and can handle input spikes with temporal information. Neuromorphic hardware designed in 40nm CMOS exploits the spike sparsity and demonstrates high classification accuracy (>98% on MNIST) and low energy (48.4-773 nJ/image).Comment: 2017 IEEE Biomedical Circuits and Systems (BioCAS

Design and implementation of the Quarc network on-chip

Networks-on-Chip (NoC) have emerged as alternative to buses to provide a packet-switched communication medium for modular development of large Systems-on-Chip. However, to successfully replace its predecessor, the NoC has to be able to efficiently exchange all types of traffic including collective communications. The latter is especially important for e.g. cache updates in multicore systems. The Quarc NoC architecture has been introduced as a Networks-on-Chip which is highly efficient in exchanging all types of traffic including broadcast and multicast. In this paper we present the hardware implementation of the switch architecture and the network adapter (transceiver) of the Quarc NoC. Moreover, the paper presents an analysis and comparison of the cost and performance between the Quarc and the Spidergon NoCs implemented in Verilog targeting the Xilinx Virtex FPGA family. We demonstrate a dramatic improvement in performance over the Spidergon especially for broadcast traffic, at no additional hardware cost

Performance analysis of a hardware accelerator of dependence management for taskbased dataflow programming models

Along with the popularity of multicore and manycore, task-based dataflow programming models obtain great attention for being able to extract high parallelism from applications without exposing the complexity to programmers. One of these pioneers is the OpenMP Superscalar (OmpSs). By implementing dynamic task dependence analysis, dataflow scheduling and out-of-order execution in runtime, OmpSs achieves high performance using coarse and medium granularity tasks. In theory, for the same application, the more parallel tasks can be exposed, the higher possible speedup can be achieved. Yet this factor is limited by task granularity, up to a point where the runtime overhead outweighs the performance increase and slows down the application. To overcome this handicap, Picos was proposed to support task-based dataflow programming models like OmpSs as a fast hardware accelerator for fine-grained task and dependence management, and a simulator was developed to perform design space exploration. This paper presents the very first functional hardware prototype inspired by Picos. An embedded system based on a Zynq 7000 All-Programmable SoC is developed to study its capabilities and possible bottlenecks. Initial scalability and hardware consumption studies of different Picos designs are performed to find the one with the highest performance and lowest hardware cost. A further thorough performance study is employed on both the prototype with the most balanced configuration and the OmpSs software-only alternative. Results show that our OmpSs runtime hardware support significantly outperforms the software-only implementation currently available in the runtime system for finegrained tasks.This work is supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272) and by the European Research Council RoMoL Grant Agreement number 321253. We also thank the Xilinx University Program for its hardware and software donations.Peer ReviewedPostprint (published version

The UTMOST pulsar timing programme I: overview and first results

We present an overview and the first results from a large-scale pulsar timing programme that is part of the UTMOST project at the refurbished Molonglo Observatory Synthesis Radio Telescope (MOST) near Canberra, Australia. We currently observe more than 400 mainly bright southern radio pulsars with up to daily cadences. For 205 (8 in binaries, 4 millisecond pulsars) we publish updated timing models, together with their flux densities, flux density variability, and pulse widths at 843 MHz, derived from observations spanning between 1.4 and 3 yr. In comparison with the ATNF pulsar catalogue, we improve the precision of the rotational and astrometric parameters for 123 pulsars, for 47 by at least an order of magnitude. The time spans between our measurements and those in the literature are up to 48 yr, which allows us to investigate their long-term spin-down history and to estimate proper motions for 60 pulsars, of which 24 are newly determined and most are major improvements. The results are consistent with interferometric measurements from the literature. A model with two Gaussian components centred at 139 and $463~\text{km} \: \text{s}^{-1}$ fits the transverse velocity distribution best. The pulse duty cycle distributions at 50 and 10 per cent maximum are best described by log-normal distributions with medians of 2.3 and 4.4 per cent, respectively. We discuss two pulsars that exhibit spin-down rate changes and drifting subpulses. Finally, we describe the autonomous observing system and the dynamic scheduler that has increased the observing efficiency by a factor of 2-3 in comparison with static scheduling.Comment: 31 pages, 14 figures, 6 tables, accepted for publication in MNRA