Kinetic Electron and Ion Instability of the Lunar Wake Simulated at Physical Mass Ratio

The solar wind wake behind the moon is studied with 1D electrostatic particle-in-cell (PIC) simulations using a physical ion to electron mass ratio (unlike prior investigations); the simulations also apply more generally to supersonic flow of dense magnetized plasma past non-magnetic objects. A hybrid electrostatic Boltzmann electron treatment is first used to investigate the ion stability in the absence of kinetic electron effects, showing that the ions are two-stream unstable for downstream wake distances (in lunar radii) greater than about three times the solar wind Mach number. Simulations with PIC electrons are then used to show that kinetic electron effects can lead to disruption of the ion beams at least three times closer to the moon than in the hybrid simulations. This disruption occurs as the result of a novel wake phenomenon: the non-linear growth of electron holes spawned from a narrow dimple in the electron velocity distribution. Most of the holes arising from the dimple are small and quickly leave the wake, approximately following the unperturbed electron phase-space trajectories, but some holes originating near the center of the wake remain and grow large enough to trigger disruption of the ion beams. Non-linear kinetic-electron effects are therefore essential to a comprehensive understanding of the 1D electrostatic stability of such wakes, and possible observational signatures in ARTEMIS data from the lunar wake are discussed.Comment: 9 pages, 10 figure

Plasma electron hole oscillatory velocity instability

In this paper, we report a new type of instability of electron holes (EHs) interacting with passing ions. The nonlinear interaction of EHs and ions is investigated using a new theory of hole kinematics. It is shown that the oscillation in the velocity of the EH parallel to the magnetic field direction becomes unstable when the hole velocity in the ion frame is slower than a few times the cold ion sound speed. This instability leads to the emission of ion-acoustic waves from the solitary hole and decay in its magnitude. The instability mechanism can drive significant perturbations in the ion density. The instability threshold, oscillation frequency and instability growth rate derived from the theory yield quantitative agreement with the observations from a novel high-fidelity hole-tracking particle-in-cell code

UDC: Unified DNAS for Compressible TinyML Models

Deploying TinyML models on low-cost IoT hardware is very challenging, due to limited device memory capacity. Neural processing unit (NPU) hardware address the memory challenge by using model compression to exploit weight quantization and sparsity to fit more parameters in the same footprint. However, designing compressible neural networks (NNs) is challenging, as it expands the design space across which we must make balanced trade-offs. This paper demonstrates Unified DNAS for Compressible (UDC) NNs, which explores a large search space to generate state-of-the-art compressible NNs for NPU. ImageNet results show UDC networks are up to $3.35\times$ smaller (iso-accuracy) or 6.25% more accurate (iso-model size) than previous work

Active multi-fidelity Bayesian online changepoint detection

Online algorithms for detecting changepoints, or abrupt shifts in the behavior of a time series, are often deployed with limited resources, e.g., to edge computing settings such as mobile phones or industrial sensors. In these scenarios it may be beneficial to trade the cost of collecting an environmental measurement against the quality or "fidelity" of this measurement and how the measurement affects changepoint estimation. For instance, one might decide between inertial measurements or GPS to determine changepoints for motion. A Bayesian approach to changepoint detection is particularly appealing because we can represent our posterior uncertainty about changepoints and make active, cost-sensitive decisions about data fidelity to reduce this posterior uncertainty. Moreover, the total cost could be dramatically lowered through active fidelity switching, while remaining robust to changes in data distribution. We propose a multi-fidelity approach that makes cost-sensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints. We evaluate this framework on synthetic, video, and audio data and show that this information-based approach results in accurate predictions while reducing total cost.Comment: 37th Conference on Uncertainty in Artificial Intelligenc

PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices

The ability to accurately predict deep neural network (DNN) inference performance metrics, such as latency, power, and memory footprint, for an arbitrary DNN on a target hardware platform is essential to the design of DNN based models. This ability is critical for the (manual or automatic) design, optimization, and deployment of practical DNNs for a specific hardware deployment platform. Unfortunately, these metrics are slow to evaluate using simulators (where available) and typically require measurement on the target hardware. This work describes PerfSAGE, a novel graph neural network (GNN) that predicts inference latency, energy, and memory footprint on an arbitrary DNN TFlite graph (TFL, 2017). In contrast, previously published performance predictors can only predict latency and are restricted to pre-defined construction rules or search spaces. This paper also describes the EdgeDLPerf dataset of 134,912 DNNs randomly sampled from four task search spaces and annotated with inference performance metrics from three edge hardware platforms. Using this dataset, we train PerfSAGE and provide experimental results that demonstrate state-of-the-art prediction accuracy with a Mean Absolute Percentage Error of <5% across all targets and model search spaces. These results: (1) Outperform previous state-of-art GNN-based predictors (Dudziak et al., 2020), (2) Accurately predict performance on accelerators (a shortfall of non-GNN-based predictors (Zhang et al., 2021)), and (3) Demonstrate predictions on arbitrary input graphs without modifications to the feature extractor

Computational and theoretical study of electron phase-space holes in kinetic plasma: kinematics, stability and ion coupling

Thesis: Ph. D. in Applied Plasma Physics, Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2018.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 (pages 181-191).In this thesis, a comprehensive study of Bernstein-Greene-Kruskal (BGK) mode electron holes in a collisionless plasma where strong kinetic effects are important is presented. Kinematic theory based on momentum conservation is derived treating the electron hole as a composite object to study the dynamics of electron holes. A novel 1-D Particle-In-Cell simulation code that can self-consistently track the electron hole motion has been developed for the purpose of this thesis work. Quantitative agreement is achieved between analytic theory and simulation observations. The thesis reports a new kind of instability for electron holes. Slow electron holes traveling slower than a few times the cold ion sound speed in the ion frame are observed to be unstable to the oscillatory velocity instability. A complete theoretical treatment for the instability is presented in this thesis. Numerical simulations yield quantitative agreement with the analytic theory in instability thresholds, frequencies and partially in instability growth rates. It is further shown that an electron hole can form a stable Coupled Hole Soliton (CHS) pair with an ion-acoustic soliton. A stable CHS travels slightly faster than the ion-acoustic velocity in the ion frame and is separated from a typical BGK mode electron hole in the velocity range by a gap, which is set by the oscillatory velocity instability. Transition between the two states is possible in both directions. A CHS exhibits a soliton-like behavior. The thesis sheds light on solving the ambiguity between an electron hole and a soliton. This thesis work also has important implications for interpreting space probes observations.by Chuteng Zhou.Ph. D. in Applied Plasma Physic

Plasma electron hole kinematics. I. Momentum conservation

We analyse the kinematic properties of a plasma electron hole: a non-linear self-sustained localized positive electric potential perturbation, trapping electrons, which behaves as a coherent entity. When a hole accelerates or grows in depth, ion and electron plasma momentum is changed both within the hole and outside, by an energization process we call jetting. We present a comprehensive analytic calculation of the momentum changes of an isolated general one-dimensional hole. The conservation of the total momentum gives the hole's kinematics, determining its velocity evolution. Our results explain many features of the behavior of hole speed observed in numerical simulations, including self-acceleration at formation, and hole pushing and trapping by ion streams.National Science Foundation (U.S.) (Grant DE-SC0010491)United States. Department of Energy (Grant DE-SC0010491