74 research outputs found
Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM
In the past few years, more and more AI applications have been applied to
edge devices. However, models trained by data scientists with machine learning
frameworks, such as PyTorch or TensorFlow, can not be seamlessly executed on
edge. In this paper, we develop an end-to-end code generator parsing a
pre-trained model to C source libraries for the backend using MicroTVM, a
machine learning compiler framework extension addressing inference on bare
metal devices. An analysis shows that specific compute-intensive operators can
be easily offloaded to the dedicated accelerator with a Universal Modular
Accelerator (UMA) interface, while others are processed in the CPU cores. By
using the automatically generated ahead-of-time C runtime, we conduct a hand
gesture recognition experiment on an ARM Cortex M4F core.Comment: CODAI 2022 Workshop - Embedded System Week (ESWeek
Measuring the Local Twist Angle and Layer Arrangement in Van der Waals Heterostructures
The properties of Van der Waals heterostructures are determined by the twist
angle and the interface between adjacent layers as well as their polytype and
stacking. Here we describe the use of spectroscopic Low Energy Electron
Microscopy (LEEM) and micro Low Energy Electron Diffraction ({\mu}LEED) methods
to measure these properties locally. We present results on a MoS/hBN
heterostructure, but the methods are applicable to other materials. Diffraction
spot analysis is used to assess the benefits of using hBN as a substrate. In
addition, by making use of the broken rotational symmetry of the lattice, we
determine the cleaving history of the MoS flake, i.e., which layer stems
from where in the bulk
Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis
For many complex materials systems, low-energy electron microscopy (LEEM)
offers detailed insights into morphology and crystallography by naturally
combining real-space and reciprocal-space information. Its unique strength,
however, is that all measurements can easily be performed energy-dependently.
Consequently, one should treat LEEM measurements as multi-dimensional,
spectroscopic datasets rather than as images to fully harvest this potential.
Here we describe a measurement and data analysis approach to obtain such
quantitative spectroscopic LEEM datasets with high lateral resolution. The
employed detector correction and adjustment techniques enable measurement of
true reflectivity values over four orders of magnitudes of intensity. Moreover,
we show a drift correction algorithm, tailored for LEEM datasets with inverting
contrast, that yields sub-pixel accuracy without special computational demands.
Finally, we apply dimension reduction techniques to summarize the key
spectroscopic features of datasets with hundreds of images into two single
images that can easily be presented and interpreted intuitively. We use cluster
analysis to automatically identify different materials within the field of view
and to calculate average spectra per material. We demonstrate these methods by
analyzing bright-field and dark-field datasets of few-layer graphene grown on
silicon carbide and provide a high-performance Python implementation
Earth system modeling with endogenous and dynamic human societies: the copan:CORE open World-Earth modeling framework
Analysis of Earth system dynamics in the Anthropocene requires to explicitly
take into account the increasing magnitude of processes operating in human
societies, their cultures, economies and technosphere and their growing
feedback entanglement with those in the physical, chemical and biological
systems of the planet. However, current state-of-the-art Earth System Models do
not represent dynamic human societies and their feedback interactions with the
biogeophysical Earth system and macroeconomic Integrated Assessment Models
typically do so only with limited scope. This paper (i) proposes design
principles for constructing World-Earth Models (WEM) for Earth system analysis
of the Anthropocene, i.e., models of social (World) - ecological (Earth)
co-evolution on up to planetary scales, and (ii) presents the copan:CORE open
simulation modeling framework for developing, composing and analyzing such WEMs
based on the proposed principles. The framework provides a modular structure to
flexibly construct and study WEMs. These can contain biophysical (e.g. carbon
cycle dynamics), socio-metabolic/economic (e.g. economic growth) and
socio-cultural processes (e.g. voting on climate policies or changing social
norms) and their feedback interactions, and are based on elementary entity
types, e.g., grid cells and social systems. Thereby, copan:CORE enables the
epistemic flexibility needed for contributions towards Earth system analysis of
the Anthropocene given the large diversity of competing theories and
methodologies used for describing socio-metabolic/economic and socio-cultural
processes in the Earth system by various fields and schools of thought. To
illustrate the capabilities of the framework, we present an exemplary and
highly stylized WEM implemented in copan:CORE that illustrates how endogenizing
socio-cultural processes and feedbacks could fundamentally change macroscopic
model outcomes
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