We consider the problem of resource provisioning for real-time cyber-physical applications in an open system environment where there does not exist a global resource scheduler that has complete knowledge of the real-time performance requirements of each individual application that shares the resources with the other applications. Regularity-based Resource Partition (RRP) model is an effective strategy to hierarchically partition and assign various resource slices among such applications. However, previous work on RRP model only discusses uniform resource environment, where resources are implicitly assumed to be synchronized and clocked at the same frequency. The challenge is that a task utilizing multiple resources may experience unexpected delays in non-uniform environments, where resources are clocked at different frequencies. This paper extends the RRP model to non-uniform multi-resource open system environments to tackle this problem. It first introduces a novel composite resource partition abstraction and then proposes algorithms to construct and reconfigure the composite resource partitions. Specifically, the
- Award ID(s):
- 1733701
- PAR ID:
- 10066593
- Date Published:
- Journal Name:
- 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)
- Page Range / eLocation ID:
- 877 to 880
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Acyclic Regular Composite Resource Partition Scheduling (ARCRP-S) algorithm constructs regular composite resource partitions and theAcyclic Regular Composite Resource Partition Dynamic Reconfiguration (ARCRP-DR) algorithm reconfigures the composite resource partitions in the run time upon requests of partition configuration changes. Our experimental results show that compared with state-of-the-art methods, ARCRP-S can prevent unexpected resource supply shortfall and improve the schedulability up to 50%. On the other hand, ARCRP-DR can guarantee the resource supply during the reconfiguration with moderate computational overhead. -
Field programmable gate array (FPGA) is widely used in the acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, conventional FPGA suffers from the trade-off between chip area and reconfiguration latency, making efficient FPGA accelerations that require switching between multiple configurations still elusive. Here, we propose a ferroelectric field-effect transistor (FeFET)–based context-switching FPGA supporting dynamic reconfiguration to break this trade-off, enabling loading of arbitrary configuration without interrupting the active configuration execution. Leveraging the intrinsic structure and nonvolatility of FeFETs, compact FPGA primitives are proposed and experimentally verified. The evaluation results show our design shows a 63.0%/74.7% reduction in a look-up table (LUT)/connection block (CB) area and 82.7%/53.6% reduction in CB/switch box power consumption with a minimal penalty in the critical path delay (9.6%). Besides, our design yields significant time savings by 78.7 and 20.3% on average for context-switching and dynamic reconfiguration applications, respectively.
-
4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances.more » « less
-
Abstract The High-Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory, located on the side of the Sierra Negra volcano in Mexico, has been fully operational since 2015. The HAWC collaboration has recently significantly improved their extensive air shower reconstruction algorithms, which has notably advanced the observatory performance. The energy resolution for primary gamma rays with energies below 1 TeV was improved by including a noise-suppression algorithm. Corrections have also been made to systematic errors in direction fitting related to the detector and shower plane inclinations,
biases in highly inclined showers, and enhancements to the core reconstruction. The angular resolution for gamma rays approaching the HAWC array from large zenith angles (>37°) has improved by a factor of 4 at the highest energies (>70 TeV) as compared to previous reconstructions. The inclusion of a lateral distribution function fit to the extensive air shower footprint on the array to separate gamma-ray primaries from cosmic-ray ones based on the resultingχ 2values improved the background rejection performance at all inclinations. At large zenith angles, the improvement in significance is a factor of 4 compared to previous HAWC publications. These enhancements have been verified by observing the Crab Nebula, which is an overhead source for the HAWC Observatory. We show that the sensitivity to Crab-like point sources (E −2.63) with locations overhead to 30° zenith is comparable to or less than 10% of the Crab Nebula’s flux between 2 and 50 TeV. Thanks to these improvements, HAWC can now detect more sources, including the Galactic center. -
Space-variant control of optical wavefronts is important for many applications in photonics, such as the generation of structured light beams, and is achieved with spatial light modulators. Commercial devices, at present, are based on liquid-crystal and digital micromirror technologies and are typically limited to kilohertz switching speeds. To realize significantly higher operating speeds, new technologies and approaches are necessary. Here we demonstrate two-dimensional control of free-space optical fields at a wavelength of 1,550 nm at a 1 GHz modulation speed using a programmable plasmonic phase modulator based on near-field interactions between surface plasmons and materials with an electrooptic response. High χ(2) and χ(3) dielectric thin films of either aluminium nitride or silicon-rich silicon nitride are used as an active modulation layer in a surface plasmon resonance configuration to realize programmable space-variant control of optical wavefronts in a 4 × 4 pixel array at high speed.more » « less