Thermal Characterization of Next-Generation Workloads on Heterogeneous MPSoCs

Next-generation High-Performance Computing (HPC) applications need to tackle outstanding computational complexity while meeting latency and Quality-of-Service constraints. Heterogeneous Multi-Processor Systems-on-Chip (MPSoCs), equipped with a mix of general-purpose cores and reconfigurable fabric for custom acceleration of computational blocks, are key in providing the flexibility to meet the requirements of next-generation HPC. However, heterogeneity brings new challenges to efficient chip thermal management. In this context, accurate and fast thermal simulators are becoming crucial to understand and exploit the trade-offs brought by heterogeneous MPSoCs. In this paper, we first thermally characterize a next-generation HPC workload, the online video transcoding application, using a highly-accurate Infra-Red (IR) microscope. Second, we extend the 3D-ICE thermal simulation tool with a new generic heat spreader model capable of accurately reproducing package surface temperature, with an average error of 6.8% for the hot spots of the chip. Our model is used to characterize the thermal behaviour of the online transcoding application when running on a heterogeneous MPSoC. Moreover, by using our detailed thermal system characterization we are able to explore different application mappings as well as the thermal limits of such heterogeneous platforms

Locomotion training of legged robots using hybrid machine learning techniques

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results

Distinguishing coherent and thermal photon noise in a circuit QED system

In the cavity-QED architecture, photon number fluctuations from residual cavity photons cause qubit dephasing due to the AC Stark effect. These unwanted photons originate from a variety of sources, such as thermal radiation, leftover measurement photons, and crosstalk. Using a capacitively-shunted flux qubit coupled to a transmission line cavity, we demonstrate a method that identifies and distinguishes coherent and thermal photons based on noise-spectral reconstruction from time-domain spin-locking relaxometry. Using these measurements, we attribute the limiting dephasing source in our system to thermal photons, rather than coherent photons. By improving the cryogenic attenuation on lines leading to the cavity, we successfully suppress residual thermal photons and achieve $T_1$-limited spin-echo decay time. The spin-locking noise spectroscopy technique can readily be applied to other qubit modalities for identifying general asymmetric non-classical noise spectra

Evaluation Of Sensitivity To Chemotherapeutants In Successive Generations Of Lepeoptheirus Salmonis From A Resistant Population

There are currently reports of reduced sensitivity to certain lice treatments in different parts of Scotland and world-wide, and research is on-going into the extent and mechanisms of resistance to different treatments (Denholm et al., 2002; Sevatdal & Horsberg, 2003; Sevatdal et al., 2005). In particular, increasing evidence of resistance of Lepeophtheirus salmonis to the chemotherapeutant emamectin benzoate (Lees et al., 2008; Espedal et al., 2010) poses a serious problem to commercial farms because there are few licensed and effective treatments available

High-pressure polymorphism in L-threonine between ambient pressure and 22 GPa

The crystal structure of l-threonine has been studied to a maximum pressure of 22.3 GPa using single-crystal X-ray and neutron powder diffraction. The data have been interpreted in the light of previous Raman spectroscopic data by Holanda et al. (J. Mol. Struct. (2015), 1092, 160-165) in which it is suggested that three phase transitions occur at ca. 2 GPa, between 8.2 and 9.2 GPa and between 14.0 and 15.5 GPa. In the first two of these transitions the crystal retains its P212121 symmetry, in the third, although the unit cell dimensions are similar either side of the transition, the space group symmetry drops to P21. The ambient pressure form is labelled phase I, with the successive high-pressure forms designated I′, II and III, respectively. Phases I and I′ are very similar, the transition being manifested by a slight rotation of the carboxylate group. Phase II, which was found to form between 8.5 and 9.2 GPa, follows the gradual transformation of a long-range electrostatic contact becoming a hydrogen bond between 2.0 and 8.5 GPa, so that the transformation reflects a change in the way the structure accommodates compression rather than a gross change of structure. Phase III, which was found to form above 18.2 GPa in this work, is characterised by the bifurcation of a hydroxyl group in half of the molecules in the unit cell. Density functional theory (DFT) geometry optimisations were used to validate high-pressure structural models and PIXEL crystal lattice and intermolecular interaction energies are used to explain phase stabilities in terms of the intermolecular interactions

Reflecting on One Health in Action During the COVID-19 Response

The COVID-19 pandemic, a singular disruptive event in recent human history, has required rapid, innovative, coordinated and collaborative approaches to manage and ameliorate its worst impacts. However, the threat remains, and learning from initial efforts may benefit the response management in the future. One Health approaches to managing health challenges through multi-stakeholder engagement are underscored by an enabling environment. Here we describe three case studies from state (New South Wales, Australia), national (Ireland), and international (sub-Saharan Africa) scales which illustrate different aspects of One Health in action in response to the COVID-19 pandemic. In Ireland, a One Health team was assembled to help parameterise complex mathematical and resource models. In New South Wales, state authorities engaged collaboratively with animal health veterinarians and epidemiologists to leverage disease outbreak knowledge, expertise and technical and support structures for application to the COVID-19 emergency. The African One Health University Network linked members from health institutions and universities from eight countries to provide a virtual platform knowledge exchange on COVID-19 to support the response. Themes common to successful experiences included a shared resource base, interdisciplinary engagement, communication network strategies, and looking global to address local need. The One Health approaches used, particularly shared responsibility and knowledge integration, are benefiting the management of this pandemic and future One Health global challenges