Optically levitated nanoparticle as a model system for stochastic bistable dynamics

Nano-mechanical resonators have gained an increasing importance in nanotechnology owing to their contributions to both fundamental and applied science. Yet, their small dimensions and mass raises some challenges as their dynamics gets dominated by nonlinearities that degrade their performance, for instance in sensing applications. Here, we report on the precise control of the nonlinear and stochastic bistable dynamics of a levitated nanoparticle in high vacuum. We demonstrate how it can lead to efficient signal amplification schemes, including stochastic resonance. This work contributes to showing the use of levitated nanoparticles as a model system for stochastic bistable dynamics, with applications to a wide variety of fields.inancial support from the ERC- QnanoMECA (Grant No. 64790), the Spanish Ministry of Economy and Competitiveness, under grant FIS2016-80293-R and through the ‘Severo Ochoa’ Programme for Centres of Excellence in R&D (SEV-2015-0522), Fundació Privada CELLEX and from the CERCA Programme/Generalitat de Catalunya. J.G. has been supported by H2020-MSCA-IF-2014 under REA grant Agreement No. 655369. L.R. acknowledges support from an ETH Marie Curie Cofund Fellowship

Variability-tolerant workload allocation for MPSoC energy minimization under real-time constraints

Abstract Sub-50nm CMOS technologies are affected by significant variability which causes power and performance variations among nominally similar cores in MPSoC platforms. This undesired heterogeneity threatens execution predictability and energy efficiency. We propose two techniques to allocate sets of barrier-synchronized tasks (representative of a wide class of image processing workloads) onto variability-affected MPSoCs. The first technique models allocation as an ILP and achieves optimal results, but requires an off-line solver. The second techniques adopt a two-stage heuristic approach, and it can be adapted to work on-line. We tested our approach on the virtual prototype of a next-generation industrial multi-core platform. Experimental results demonstrate that our approach minimizes deadline violations while increasing energy efficiency

An efficient on-line task allocation algorithm for QoS and energy efficiency in multicore multimedia platforms

The impact of variability on sub-45nm CMOS multimedia platforms makes hard to provide application QoS guarantees, as the speed variations across the cores may cause sub-optimal and sample-dependent utilization of the available resources and energy budget. These effects can be compensated by an efficient allocation of the workload at run-time. In the context of multimedia applications, a critical objective is to compensate core speed variability while matching time constraints without impacting the energy consumption. In this paper we present a new approach to compute optimal task allocations at run-time. The proposed strategy exploits an efficient and scalable implementation to find on-line the best possible solution in a tightly bounded time. Experimental results demonstrate the effectiveness of compensation both in terms of deadline miss rate and energy savings. Results have been compared with those obtained applying state-of-art techniques on a multithreaded MPEG2 decoder. The validation has been performed on a cycle-accurate virtual prototype of a next-generation industrial multicore platform that has been extended with process variability models

Adaptive idleness distribution for non-uniform aging tolerance in MultiProcessor Systems-on-Chip

In deep submicron designs of MultiProcessor Systems-on-Chip (MPSoC) architectures, uncompensated within-die process variations and aging effects will lead to an increasing uncertainty and unbalancing of expected core lifetimes. In this paper we present an adaptive workload allocation strategy for run-time compensation of variations- and aging-induced unbalanced core lifetimes by means of core activity duty cycling. The proposed techniques regulates the percentage of idle time on short-expected-life cores to meet the platform lifetime target with minimum performance degradation. Experiments have been conducted on a multiprocessor simulator of a next-generation industrial MPSoC platform for multimedia applications made of a general purpose processor and programmable accelerators

Variability-tolerant Run-time Workload Allocation for MPSoC Energy Minimization under Real-time Constraints

Multicore architectures will be adopted in the sub-50nm CMOS technology nodes for virtually all application domains with energy efficiency requirements exceeding 10GOPS/Watt. Unfortunately, future technology nodes will be increasingly affected by variation phenomena, and multicore architectures will be impacted in many ways by the variability of the underlying silicon fabrics [1, 6, 8]. Our architectural target is an advanced prototype of an industrial multicore platform for post-2014 set-top-box products, featuring a single CPU coordinator and an array of programmable VLIWhardware accelerators with multi-threading support. Next-generation set-top-boxes will support very high resolution, high-frame rate video rendering with complex 3D GUIs and stereoscopic visualization support [2]. These applications require extensive image processing and enhancements functions which are embarrassingly parallel and will be distributed on the VLIW accelerator array as a large number of barrier-synchronized tasks. Accelerators are nominally homogeneous, but unfortunately variability causes significant perturbations on their performance and power consumption. We define a two-phase approach based on linear programming and bin packing. Thanks to these steps, the technique performs task allocation exploiting the awareness of performance and power variations of the cores, thus minimizing deadline misses and improving energy efficiency of the platform with respect to a variation-blind approach. In this work we consider variability effects acting independently on critical path delay, leakage power, and dynamic power [3]. Variability distribution data have been obtained through the VAM tool [7]. This distribution is used to generate variability affected platforms

Embedded reconfigurable architectures

In current-day embedded systems design, one is faced with cut-throat competition to deliver new functionalities in increasingly shorter time frames. This is now achieved by incorporating processor cores into embedded systems through (re-)programmability. However, this is not always beneficial for the performance or energy consumption. Therefore, adaptable embedded systems have been proposed to deal with these negative effects by reconfiguring the critical sections of an embedded system. In these proposals, we are clearly witnessing a trend that is moving from static configurations to dynamic (re)configurations. Consequently, the proposed embedded systems can adapt their functionality at run-time to meet the application(s) requirements (e.g., performance) while operating in different environments (e.g., power and hardware resources). Besides processor cores, we have to deal with memory hierarchies and network-on-chips that should also be (dynamically) reconfigurable. Furthermore, the interplay of these components is increasing the design complexity that can be only alleviated if they can self-optimize. In this tutorial, we will present and discuss several strategies to perform the mentioned dynamic reconfiguration of the processor, memory, and NoC components - together with their interaction. We will review and present the state-of-the-art for the design of each component that allows for a gradual selection of design points in the trade-off between performance and power. Finally, we will highlight an open-source project that incorporates many approaches for dynamic reconfiguration in both actual hardware and simulation accompanied by the necessary tools

Optimized observable readout from single-shot images of ultracold atoms via machine learning

Single-shot images are the standard readout of experiments with ultracold atoms, the imperfect reflection of their many-body physics. The efficient extraction of observables from single-shot images is thus crucial. Here we demonstrate how artificial neural networks can optimize this extraction. In contrast to standard averaging approaches, machine learning allows both one- and two-particle densities to be accurately obtained from a drastically reduced number of single-shot images. Quantum fluctuations and correlations are directly harnessed to obtain physical observables for bosons in a tilted double-well potential at an extreme accuracy. Strikingly, machine learning also enables a reliable extraction of momentum-space observables from real-space single-shot images and vice versa. With this technique, the reconfiguration of the experimental setup between in situ and time-of-flight imaging is required only once to obtain training data, thus potentially granting an outstanding reduction in resources