Modeling Fission Gas Release at the Mesoscale using Multiscale DenseNet Regression with Attention Mechanism and Inception Blocks

Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a powerful tool for understanding how microstructure evolution impacts FGR, but they are computationally intensive. In this study, we present an alternate, data-driven approach, using deep learning to predict instantaneous FGR flux from 2D nuclear fuel microstructure images. Four convolutional neural network (CNN) architectures with multiscale regression are trained and evaluated on simulated FGR data generated using a hybrid phase field/cluster dynamics model. All four networks show high predictive power, with $R^{2}$ values above 98%. The best performing network combine a Convolutional Block Attention Module (CBAM) and InceptionNet mechanisms to provide superior accuracy (mean absolute percentage error of 4.4%), training stability, and robustness on very low instantaneous FGR flux values.Comment: Submitted at Journal of Nuclear Materials, 20 pages, 10 figures, 3 table

Running deep learning applications on resource constrained devices

The high accuracy of Deep Neural Networks (DNN) come at the expense of high computational cost and memory requirements. During inference, the data is often collected on the edge device which are resource-constrained. The existing solutions for edge deployment include i) executing the entire DNN on the edge (EDGE-ONLY), ii) sending the input from edge to cloud where the DNN is processed (CLOUD-ONLY), and iii) splitting the DNN to execute partially on the edge and partially on the cloud (SPLIT). The choice of deployment between EDGE-ONLY, CLOUD-ONLY and SPLIT is determined by several operating constraints such as device resources and network speed, and application constraints such as latency and accuracy. The EDGE-ONLY approach requires compact DNN with low compute and memory requirements. Thus, the emerging class of DNNs employ low-rank convolutions (LRCONVs) which reduce one or more dimensions compared to the spatial convolutions (CONV). Prior research in hardware accelerators has largely focused on CONVs. The LRCONVs such as depthwise and pointwise convolutions exhibit lower arithmetic intensity and lower data reuse. Thus, LRCONVs result in low hardware utilization and high latency. In our first work, we systematically explore the design space of Cross-layer dataflows to exploit data reuse across layers for emerging DNNs in EDGE-ONLY scenarios. We develop novel fine-grain cross-layer dataflows for LRCONVs that support partial loop dimension completion. Our tool, X-Layer decouples the nested loops in a pipeline and combines them to create a common outer dataflow and several inner dataflows. The CLOUD-ONLY approach can suffer from high latency due to the high transmission cost of large input data from the edge to the cloud. This could be a problem, especially for latency-critical applications. Thankfully, the SPLIT approach reduces latency compared to the CLOUD-ONLY approach. However, existing solutions only split the DNN in floating-point precision. Executing floating-point precision on the edge device can occupy large memory and reduce the potential options for SPLIT solutions. In our second work, we expand and explore the search space of SPLIT solutions by jointly applying mixed-precision post-training quantization and DNN graph split. Our work, Auto-Split finds a balance in the trade-off among the model accuracy, edge device capacity, transmission cost, and the overall latency

Energy-Efficient Neural Network Architectures

Emerging systems for artificial intelligence (AI) are expected to rely on deep neural networks (DNNs) to achieve high accuracy for a broad variety of applications, including computer vision, robotics, and speech recognition. Due to the rapid growth of network size and depth, however, DNNs typically result in high computational costs and introduce considerable power and performance overheads. Dedicated chip architectures that implement DNNs with high energy efficiency are essential for adding intelligence to interactive edge devices, enabling them to complete increasingly sophisticated tasks by extending battery lie. They are also vital for improving performance in cloud servers that support demanding AI computations. This dissertation focuses on architectures and circuit technologies for designing energy-efficient neural network accelerators. First, a deep-learning processor is presented for achieving ultra-low power operation. Using a heterogeneous architecture that includes a low-power always-on front-end and a selectively-enabled high-performance back-end, the processor dynamically adjusts computational resources at runtime to support conditional execution in neural networks and meet performance targets with increased energy efficiency. Featuring a reconfigurable datapath and a memory architecture optimized for energy efficiency, the processor supports multilevel dynamic activation of neural network segments, performing object detection tasks with 5.3x lower energy consumption in comparison with a static execution baseline. Fabricated in 40nm CMOS, the processor test-chip dissipates 0.23mW at 5.3 fps. It demonstrates energy scalability up to 28.6 TOPS/W and can be configured to run a variety of workloads, including severely power-constrained ones such as always-on monitoring in mobile applications. To further improve the energy efficiency of the proposed heterogeneous architecture, a new charge-recovery logic family, called zero-short-circuit current (ZSCC) logic, is proposed to decrease the power consumption of the always-on front-end. By relying on dedicated circuit topologies and a four-phase clocking scheme, ZSCC operates with significantly reduced short-circuit currents, realizing order-of-magnitude power savings at relatively low clock frequencies (in the order of a few MHz). The efficiency and applicability of ZSCC is demonstrated through an ANSI S1.11 1/3 octave filter bank chip for binaural hearing aids with two microphones per ear. Fabricated in a 65nm CMOS process, this charge-recovery chip consumes 13.8µW with a 1.75MHz clock frequency, achieving 9.7x power reduction per input in comparison with a 40nm monophonic single-input chip that represents the published state of the art. The ability of ZSCC to further increase the energy efficiency of the heterogeneous neural network architecture is demonstrated through the design and evaluation of a ZSCC-based front-end. Simulation results show 17x power reduction compared with a conventional static CMOS implementation of the same architecture.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147614/1/hsiwu_1.pd

Addressing subjectivity in the classification of palaeoenvironmental remains with supervised deep learning convolutional neural networks

Archaeological object identifications have been traditionally undertaken through a comparative methodology where each artefact is identified through a subjective, interpretative act by a professional. Regarding palaeoenvironmental remains, this comparative methodology is given boundaries by using reference materials and codified sets of rules, but subjectivity is nevertheless present. The problem with this traditional archaeological methodology is that higher level of subjectivity in the identification of artefacts leads to inaccuracies, which then increases the potential for Type I and Type II errors in the testing of hypotheses. Reducing the subjectivity of archaeological identifications would improve the statistical power of archaeological analyses, which would subsequently lead to more impactful research. In this thesis, it is shown that the level of subjectivity in palaeoenvironmental research can be reduced by applying deep learning convolutional neural networks within an image recognition framework. The primary aim of the presented research is therefore to further the on-going paradigm shift in archaeology towards model-based object identifications, particularly within the realm of palaeoenvironmental remains. Although this thesis focuses on the identification of pollen grains and animal bones, with the latter being restricted to the astragalus of sheep and goats, there are wider implications for archaeology as these methods can easily be extended beyond pollen and animal remains. The previously published POLEN23E dataset is used as the pilot study of applying deep learning in pollen grain classification. In contrast, an image dataset of modern bones was compiled for the classification of sheep and goat astragali due to a complete lack of available bone image datasets and a double blind study with inexperienced and experienced zooarchaeologists was performed to have a benchmark to which image recognition models can be compared. In both classification tasks, the presented models outperform all previous formal modelling methods and only the best human analysts match the performance of the deep learning model in the sheep and goat astragalus separation task. Throughout the thesis, there is a specific focus on increasing trust in the models through the visualization of the models’ decision making and avenues of improvements to Grad-CAM are explored. This thesis makes an explicit case for the phasing out of the comparative methods in favour of a formal modelling framework within archaeology, especially in palaeoenvironmental object identification

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio