Study on a visual coder acceleration algorithm for image classification applying dynamic scaling training techniques

Nowadays, image classification techniques are used in the field of autonomous vehicles, and Convolutional Neural Network (CNN) is used extensively, and Vision Transformer (ViT) networks are used instead of deep convolutional networks in order to compress the network size and improve the model accuracy. The ViT network is used to replace the deep convolutional network. Since training ViT requires a large dataset to have sufficient accuracy, a variant of ViT, Data-Efficient Image Transformers (DEIT), is used in this paper. In addition, in order to greatly reduce the computing memory and shorten the computing time in practical use, the network is flexibly scaled in size and training speed by both adaptive width and adaptive depth. In this paper, we introduce DEIT, width adaptive techniques and depth adaptive techniques and combine them to be applied to image classification examples. Experiments are conducted on the Cifar100 dataset, and the experiments demonstrate the superiority of the algorithm on image classification scenarios.

Abstract Rotations for Uniform Adaptive Control and Soft Modeling of Mechanical Devices

The model-based controllers generally suffer from the lack of precise dynamic models. Making reliable analytical models can be evaded by soft modeling techniques, while the consequences of modeling imprecisions are tackled by either robust or adaptive techniques. In robotics, the prevailing adaptive techniques are based on Lyapunov’s “direct method” that normally uses special error metrics and adaptation rules containing fragments of the Lyapunov function. The soft models and controllers need massive parallelism and suffer from the curse of dimensionality. A different adaptive approach based on Banach’s fixed point theorem and using special abstract rotations was recently suggested. Similar rotations were suggested to develop particular neural network-like soft models, too. Presently, via integrating these approaches, a uniform adaptive controlling and modeling methodology is suggested with especial emphasis on the effects of the measurement noises. Its applicability is investigated via simulations for a two degree of freedom mechanical system in which one of the generalized coordinates is under control, while the other one belongs to a coupled parasite dynamical system. The results are promising for allowing the development of relatively coarse soft models and a simple adaptive rule that can be implemented in embedded systems.

Multibody-Based Input and State Observers Using Adaptive Extended Kalman Filter

The aim of this work is to explore the suitability of adaptive methods for state estimators based on multibody dynamics, which present severe non-linearities. The performance of a Kalman filter relies on the knowledge of the noise covariance matrices, which are difficult to obtain. This challenge can be overcome by the use of adaptive techniques. Based on an error-extended Kalman filter with force estimation (errorEKF-FE), the adaptive method known as maximum likelihood is adjusted to fulfill the multibody requirements. This new filter is called adaptive error-extended Kalman filter (AerrorEKF-FE). In order to present a general approach, the method is tested on two different mechanisms in a simulation environment. In addition, different sensor configurations are also studied. Results show that, in spite of the maneuver conditions and initial statistics, the AerrorEKF-FE provides estimations with accuracy and robustness. The AerrorEKF-FE proves that adaptive techniques can be applied to multibody-based state estimators, increasing, therefore, their fields of application.

A Tseng-Type Algorithm with Self-Adaptive Techniques for Solving the Split Problem of Fixed Points and Pseudomonotone Variational Inequalities in Hilbert Spaces

In this paper, we survey the split problem of fixed points of two pseudocontractive operators and variational inequalities of two pseudomonotone operators in Hilbert spaces. We present a Tseng-type iterative algorithm for solving the split problem by using self-adaptive techniques. Under certain assumptions, we show that the proposed algorithm converges weakly to a solution of the split problem. An application is included.

Dynamically Adapting Page Migration Policies Based on Applications’ Memory Access Behaviors

There have been numerous studies on heterogeneous memory systems comprised of faster DRAM (e.g., 3D stacked HBM or HMC) and slower non-volatile memories (e.g., PCM, STT-RAM). However, most of these studies focused on static policies for managing data placement and migration among the different memory devices. These policies are based on the average behavior across a range of applications. Results show that these techniques do not always result in higher performance when compared to systems that do not migrate data across the devices: some applications show performance gains, but other applications show performance losses. It is possible to utilize offline analyses to identify which applications benefit from page migration (migration friendly) and use page migration only with those applications. However, we observed that several applications exhibit both migration friendly and migration unfriendly behaviors during different phases of execution supporting a need for adaptive page migration techniques. We introduce and evaluate techniques that dynamically adapt to the behavior of applications and either reduce or increase migrations, or even halt migrations. Our adaptive techniques show performance gains for both migration friendly (on average of 81% over no migrations) and unfriendly workloads (by an average of 3%): it should be remembered that previous migration techniques resulted in performance losses for unfriendly workloads.