Software-defined Design Space Exploration for an Efficient DNN Accelerator Architecture

Deep neural networks (DNNs) have been shown to outperform conventional machine learning algorithms across a wide range of applications, e.g., image recognition, object detection, robotics, and natural language processing. However, the high computational complexity of DNNs often necessitates extremely fast and efficient hardware. The problem gets worse as the size of neural networks grows exponentially. As a result, customized hardware accelerators have been developed to accelerate DNN processing without sacrificing model accuracy. However, previous accelerator design studies have not fully considered the characteristics of the target applications, which may lead to sub-optimal architecture designs. On the other hand, new DNN models have been developed for better accuracy, but their compatibility with the underlying hardware accelerator is often overlooked. In this article, we propose an application-driven framework for architectural design space exploration of DNN accelerators. This framework is based on a hardware analytical model of individual DNN operations. It models the accelerator design task as a multi-dimensional optimization problem. We demonstrate that it can be efficaciously used in application-driven accelerator architecture design. Given a target DNN, the framework can generate efficient accelerator design solutions with optimized performance and area. Furthermore, we explore the opportunity to use the framework for accelerator configuration optimization under simultaneous diverse DNN applications. The framework is also capable of improving neural network models to best fit the underlying hardware resources

Analysis of Two Robust Learning Control Schemes in the Presence of Random Iteration-Varying Noise

Abstract-This paper deals with the design problem of robust iterative learning control (ILC), in the presence of noise that is varying randomly from iteration to iteration. Two ILC schemes are considered: one adopts the previous iteration tracking error (PITE) and the other adopts the current iteration tracking error (CITE), in the updating law. For both schemes, the convergence results are obtained by using a frequency-domain approach, and a comparison between them is presented from the viewpoints of the convergence condition, robustness against plant uncertainty, and delay compensation. It shows that sufficient conditions can be derived to bound the tracking error and make its expectation monotonically convergent in the sense of L2-norm, which work effectively with robustness for all admissible plant uncertainties. Furthermore, the sufficient conditions for both schemes can also be formulated in terms of two complementary functions, which do not depend on the delay time as well as the plant uncertainty and, thus, make them convenient to be checked and solved using the frequency-domain tools. Numerical simulations are included to illustrate the effectiveness of the two proposed ILC schemes

Differential Temporal Evolution Patterns in Brain Temperature in Different Ischemic Tissues in a Monkey Model of Middle Cerebral Artery Occlusion

Brain temperature is elevated in acute ischemic stroke, especially in the ischemic penumbra (IP). We attempted to investigate the dynamic evolution of brain temperature in different ischemic regions in a monkey model of middle cerebral artery occlusion. The brain temperature of different ischemic regions was measured with proton magnetic resonance spectroscopy (1H MRS), and the evolution processes of brain temperature were compared among different ischemic regions. We found that the normal (baseline) brain temperature of the monkey brain was 37.16°C. In the artery occlusion stage, the mean brain temperature of ischemic tissue was 1.16°C higher than the baseline; however, this increase was region dependent, with 1.72°C in the IP, 1.08°C in the infarct core, and 0.62°C in the oligemic region. After recanalization, the brain temperature of the infarct core showed a pattern of an initial decrease accompanied by a subsequent increase. However, the brain temperature of the IP and oligemic region showed a monotonously and slowly decreased pattern. Our study suggests that in vivo measurement of brain temperature could help to identify whether ischemic tissue survives

Occludin S408 phosphorylation regulates tight junction protein interactions and barrier function

Occludin S408 phosphorylation regulates interactions between occludin, ZO-1, and select claudins to define tight junction molecular structure and barrier function

Robust Controller Design of Networked Control Systems with Nonlinear Uncertainties

We address robust stabilization problem for networked control systems with nonlinear uncertainties and packet losses by modelling such systems as a class of uncertain switched systems. Based on theories on switched Lyapunov functions, we derive the robustly stabilizing conditions for state feedback stabilization and design packet-loss dependent controllers by solving some matrix inequalities. A numerical example and some simulations are worked out to demonstrate the effectiveness of the proposed design method

Audit-firm serving experience heterogeneity and audit knowledge integration: Evidence from the disclosure of key audit matters

Audit practice is a team effort led by signing auditors. We examine the impact of the heterogeneity of signing auditors’ audit-firm serving experiences on the disclosure of key audit matters (KAMs). Auditors with more heterogeneous serving experiences demonstrate more adequate KAM disclosure, as evidenced by more KAMs, longer texts and clearer attributions in their disclosures. This effect is influenced by the quality of audit knowledge that auditors accumulate from different serving experiences and the team- and audit-firm-level knowledge integration environment. Furthermore, signing auditors with more diverse service experience tend to improve audit quality, reduce the incidence of restatement or misconduct and enhance the informativeness of financial reports. Our findings enrich the KAM disclosure research and provide insights into audit firms’ human resource allocation and internal management