Learning transfer-based adaptive energy minimization in embedded systems

Embedded systems execute applications with different performance requirements. These applications exercise the hardware differently depending on the types of computation being carried out, generating varying workloads with time. We will demonstrate that energy minimization with such workload and performance variations within (intra) and across (inter) applications is particularly challenging. To address this challenge we propose an online energy minimization approach, capable of minimizing energy through adaptation to these variations. At the core of the approach is an initial learning through reinforcement learning algorithm that suitably selects the appropriate voltage/frequency scalings (VFS) based on workload predictions to meet the applications’ performance requirements. The adaptation is then facilitated and expedited through learning transfer, which uses the interaction between the system application, runtime and hardware layers to adjust the power control levers. The proposed approach is implemented as a power governor in Linux and validated on an ARM Cortex-A8 running different benchmark applications. We show that with intra- and inter-application variations, our proposed approach can effectively minimize energy consumption by up to 33% compared to existing approaches. Scaling the approach further to multi-core systems, we also show that it can minimize energy by up to 18% with 2X reduction in the learning time when compared with a recently reported approach

Distributed Video Coding: Selecting the Most Promising Application Scenarios

Distributed Video Coding (DVC) is a new video coding paradigm based on two major Information Theory results: the Slepian–Wolf and Wyner–Ziv theorems. Recently, practical DVC solutions have been proposed with promising results; however, there is still a need to study in a more systematic way the set of application scenarios for which DVC may bring major advantages. This paper intends to contribute for the identification of the most DVC friendly application scenarios, highlighting the expected benefits and drawbacks for each studied scenario. This selection is based on a proposed methodology which involves the characterization and clustering of the applications according to their most relevant characteristics, and their matching with the main potential DVC benefits

Employing Information and Communications Technologies in Homes and Cities for the Health and Well-Being of Older People

YesHe X and Sheriff RE (Eds.) Employing ICT in Homes and Cities for the Health and Well-Being of Older People. Workshop Proceedings of ICT4HOP’16. 15-17 Aug 2016. Sichuan University, Chengdu, China.British Council, Researcher Links, Newton Fund, NSF

Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

Adaptive Resource Management for Uncertain Execution Platforms

Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible. This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high

On evolution of CMOS image sensors

CMOS Image Sensors have become the principal technology in majority of digital cameras. They started replacing the film and Charge Coupled Devices in the last decade with the promise of lower cost, lower power requirement, higher integration and the potential of focal plane processing. However, the principal factor behind their success has been the ability to utilise the shrinkage in CMOS technology to make smaller pixels, and thereby have more resolution without increasing the cost. With the market of image sensors exploding courtesy their inte- gration with communication and computation devices, technology developers improved the CMOS processes to have better optical performance. Nevertheless, the promises of focal plane processing as well as on-chip integration have not been fulfilled. The market is still being pushed by the desire of having higher number of pixels and better image quality, however, differentiation is being difficult for any image sensor manufacturer. In the paper, we will explore potential disruptive growth directions for CMOS Image sensors and ways to achieve the same

Dynamic thermal management in chip multiprocessor systems

Recently, processor power density has been increasing at an alarming rate result- ing in high on-chip temperature. Higher temperature increases current leakage and causes poor reliability. In our research, we ¯rst propose a Predictive Dynamic Ther- mal Management (PDTM) based on Application-based Thermal Model (ABTM) and Core-based Thermal Model (CBTM) in the multicore systems. Based on predicted temperature from ABTM and CBTM, the proposed PDTM can maintain the system temperature below a desired level by moving the running application from the possi- ble overheated core to the future coolest core (migration) and reducing the processor resources (priority scheduling) within multicore systems. Furthermore, we present the Thermal Correlative Thermal Management (TCDTM), which incorporates three main components: Statistical Workload Estimation (SWE), Future Temperature Estima- tion Model (FTEM) and Temperature-Aware Thread Controller (TATC), to model the thermal correlation e®ect and distinguish the thermal contributions from appli- cations with di®erent workload behaviors at run time in the CMP systems. The pro- posed PDTM and TCDTM enable the exploration of the tradeo® between throughput and fairness in temperature-constrained multicore systems