303 research outputs found
Harnessing function from form: towards bio-inspired artificial intelligence in neuronal substrates
Despite the recent success of deep learning, the mammalian brain is still unrivaled when it comes
to interpreting complex, high-dimensional data streams like visual, auditory and somatosensory stimuli.
However, the underlying computational principles allowing the brain to deal with unreliable, high-dimensional
and often incomplete data while having a power consumption on the order of a few watt are still mostly
unknown.
In this work, we investigate how specific functionalities emerge from simple structures observed in the
mammalian cortex, and how these might be utilized in non-von Neumann devices like âneuromorphic
hardwareâ. Firstly, we show that an ensemble of deterministic, spiking neural networks can be shaped by
a simple, local learning rule to perform sampling-based Bayesian inference. This suggests a coding scheme
where spikes (or âaction potentialsâ) represent samples of a posterior distribution, constrained by sensory
input, without the need for any source of stochasticity. Secondly, we introduce a top-down framework where
neuronal and synaptic dynamics are derived using a least action principle and gradient-based minimization.
Combined, neurosynaptic dynamics approximate real-time error backpropagation, mappable to mechanistic
components of cortical networks, whose dynamics can again be described within the proposed framework.
The presented models narrow the gap between well-defined, functional algorithms and their biophysical
implementation, improving our understanding of the computational principles the brain might employ.
Furthermore, such models are naturally translated to hardware mimicking the vastly parallel neural
structure of the brain, promising a strongly accelerated and energy-efficient implementation of powerful
learning and inference algorithms, which we demonstrate for the physical model system âBrainScaleSâ1â
Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks
Neural networks are widely deployed models across many scientific disciplines
and commercial endeavors ranging from edge computing and sensing to large-scale
signal processing in data centers. The most efficient and well-entrenched
method to train such networks is backpropagation, or reverse-mode automatic
differentiation. To counter an exponentially increasing energy budget in the
artificial intelligence sector, there has been recent interest in analog
implementations of neural networks, specifically nanophotonic neural networks
for which no analog backpropagation demonstration exists. We design
mass-manufacturable silicon photonic neural networks that alternately cascade
our custom designed "photonic mesh" accelerator with digitally implemented
nonlinearities. These reconfigurable photonic meshes program computationally
intensive arbitrary matrix multiplication by setting physical voltages that
tune the interference of optically encoded input data propagating through
integrated Mach-Zehnder interferometer networks. Here, using our packaged
photonic chip, we demonstrate in situ backpropagation for the first time to
solve classification tasks and evaluate a new protocol to keep the entire
gradient measurement and update of physical device voltages in the analog
domain, improving on past theoretical proposals. Our method is made possible by
introducing three changes to typical photonic meshes: (1) measurements at
optical "grating tap" monitors, (2) bidirectional optical signal propagation
automated by fiber switch, and (3) universal generation and readout of optical
amplitude and phase. After training, our classification achieves accuracies
similar to digital equivalents even in presence of systematic error. Our
findings suggest a new training paradigm for photonics-accelerated artificial
intelligence based entirely on a physical analog of the popular backpropagation
technique.Comment: 23 pages, 10 figure
Intelligent shop scheduling for semiconductor manufacturing
Semiconductor market sales have expanded massively to more than 200 billion dollars annually accompanied by increased pressure on the manufacturers to provide higher quality products at lower cost to remain competitive. Scheduling of semiconductor manufacturing is one of the keys to increasing productivity, however the complexity of manufacturing high capacity semiconductor devices and the cost considerations mean that it is impossible to experiment within the facility. There is an immense need for effective decision support models, characterizing and analyzing the manufacturing process, allowing the effect of changes in the production environment to be predicted in order to increase utilization and enhance system performance. Although many simulation models have been developed within semiconductor manufacturing very little research on the simulation of the photolithography process has been reported even though semiconductor manufacturers have recognized that the scheduling of photolithography is one of the most important and challenging tasks due to complex nature of the process.
Traditional scheduling techniques and existing approaches show some benefits for solving small and medium sized, straightforward scheduling problems. However, they have had limited success in solving complex scheduling problems with stochastic elements in an economic timeframe. This thesis presents a new methodology combining advanced solution approaches such as simulation, artificial intelligence, system modeling and Taguchi methods, to schedule a photolithography toolset. A new structured approach was developed to effectively support building the simulation models. A single tool and complete toolset model were developed using this approach and shown to have less than 4% deviation from actual production values. The use of an intelligent scheduling agent for the toolset model shows an average of 15% improvement in simulated throughput time and is currently in use for scheduling the photolithography toolset in a manufacturing plant
A Spectral Condition for Feature Learning
The push to train ever larger neural networks has motivated the study of
initialization and training at large network width. A key challenge is to scale
training so that a network's internal representations evolve nontrivially at
all widths, a process known as feature learning. Here, we show that feature
learning is achieved by scaling the spectral norm of weight matrices and their
updates like , in contrast to widely
used but heuristic scalings based on Frobenius norm and entry size. Our
spectral scaling analysis also leads to an elementary derivation of
\emph{maximal update parametrization}. All in all, we aim to provide the reader
with a solid conceptual understanding of feature learning in neural networks
A Scalable Workflow for a Configurable Neuromorphic Platform
This thesis establishes a scalable multi-user workflow for the operation of a highly configurable,
large-scale neuromorphic hardware platform. The resulting software framework provides unified
low-level as well as parallel high-level access. The latter is realized by an efficient abstract
neural network description library, an automated translation of networks into hardware specific
configurations and an experiment server infrastructure responsible for scheduling and executing
experiments. Scalability, manual guidance and a broad support for handling hardware imper-
fections render the model translation process suitable for large networks as well as large-scale
neuromorphic systems. Networks with local connectivity, random networks and cortical column
models are explored to study the topological aptitude of the neuromorphic platform and to
benchmark the workflow. Depending on the model, performance improvements of more than
two orders of magnitude have been achieved over a previous implementation. Additionally, an
automated defect assessment for hardware synapses is introduced, indicating that most synapses
are available for model emulation.
In a second study, a tempotron-based hardware liquid state machine has been developed
and applied to different tasks, including a memory challenge and digit recognition. The trained
tempotron inherently compensates for fixed pattern variations making the setup suitable for
analog neuromorphic hardware. The achieved performance is comparable to reference software
simulations
New Approaches in Automation and Robotics
The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book
NASA Space Engineering Research Center Symposium on VLSI Design
The NASA Space Engineering Research Center (SERC) is proud to offer, at its second symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories and the electronics industry. These featured speakers share insights into next generation advances that will serve as a basis for future VLSI design. Questions of reliability in the space environment along with new directions in CAD and design are addressed by the featured speakers
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Improving process monitoring and modeling of batch-type plasma etching tools
Manufacturing equipments in semiconductor factories (fabs) provide abundant data and opportunities for data-driven process monitoring and modeling. In particular, virtual metrology (VM) is an active area of research. Traditional monitoring techniques using univariate statistical process control charts do not provide immediate feedback to quality excursions, hindering the implementation of fab-wide advanced process control initiatives. VM models or inferential sensors aim to bridge this gap by predicting of quality measurements instantaneously using tool fault detection and classification (FDC) sensor measurements. The existing research in the field of inferential sensor and VM has focused on comparing regressions algorithms to demonstrate their feasibility in various applications. However, two important areas, data pretreatment and post-deployment model maintenance, are usually neglected in these discussions. Since it is well known that the industrial data collected is of poor quality, and that the semiconductor processes undergo drifts and periodic disturbances, these two issues are the roadblocks in furthering the adoption of inferential sensors and VM models. In data pretreatment, batch data collected from FDC systems usually contain inconsistent trajectories of various durations. Most analysis techniques requires the data from all batches to be of same duration with similar trajectory patterns. These inconsistencies, if unresolved, will propagate into the developed model and cause challenges in interpreting the modeling results and degrade model performance. To address this issue, a Constrained selective Derivative Dynamic Time Warping (CsDTW) method was developed to perform automatic alignment of trajectories. CsDTW is designed to preserve the key features that characterizes each batch and can be solved efficiently in polynomial time. Variable selection after trajectory alignment is another topic that requires improvement. To this end, the proposed Moving Window Variable Importance in Projection (MW-VIP) method yields a more robust set of variables with demonstrably more long-term correlation with the predicted output. In model maintenance, model adaptation has been the standard solution for dealing with drifting processes. However, most case studies have already preprocessed the model update data offline. This is an implicit assumption that the adaptation data is free of faults and outliers, which is often not true for practical implementations. To this end, a moving window scheme using Total Projection to Latent Structure (T-PLS) decomposition screens incoming updates to separate the harmless process noise from the outliers that negatively affects the model. The integrated approach was demonstrated to be more robust. In addition, model adaptation is very inefficient when there are multiplicities in the process, multiplicities could occur due to process nonlinearity, switches in product grade, or different operating conditions. A growing structure multiple model system using local PLS and PCA models have been proposed to improve model performance around process conditions with multiplicity. The use of local PLS and PCA models allows the method to handle a much larger set of inputs and overcome several challenges in mixture model systems. In addition, fault detection sensitivities are also improved by using the multivariate monitoring statistics of these local PLS/PCA models. These proposed methods are tested on two plasma etch data sets provided by Texas Instruments. In addition, a proof of concept using virtual metrology in a controller performance assessment application was also tested.Chemical Engineerin
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