298 research outputs found
Power Consumption and Energy Estimation in Smartphones
A developer needs to evaluate software performance metrics such as power consumption at an early stage of design phase to make a device or a software efficient especially in real-time embedded systems. Constructing performance models and evaluation techniques of a given system requires a significant effort. This paper presents a framework to bridge between a Functional Modeling Approach such as FSM, UML etc. and an Analytical (Mathematical) Modeling Approach such as Hierarchical Performance Modeling (HPM) as a technique to find the expected average power consumption for different layers of abstractions. A Hierarchical Generic FSM “HGFSM” is developed to be used in order to estimate the expected average power. A case study is presented to illustrate the concepts of how the framework is used to estimate the average power and energy produced
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Despite the soaring use of convolutional neural networks (CNNs) in mobile
applications, uniformly sustaining high-performance inference on mobile has
been elusive due to the excessive computational demands of modern CNNs and the
increasing diversity of deployed devices. A popular alternative comprises
offloading CNN processing to powerful cloud-based servers. Nevertheless, by
relying on the cloud to produce outputs, emerging mission-critical and
high-mobility applications, such as drone obstacle avoidance or interactive
applications, can suffer from the dynamic connectivity conditions and the
uncertain availability of the cloud. In this paper, we propose SPINN, a
distributed inference system that employs synergistic device-cloud computation
together with a progressive inference method to deliver fast and robust CNN
inference across diverse settings. The proposed system introduces a novel
scheduler that co-optimises the early-exit policy and the CNN splitting at run
time, in order to adapt to dynamic conditions and meet user-defined
service-level requirements. Quantitative evaluation illustrates that SPINN
outperforms its state-of-the-art collaborative inference counterparts by up to
2x in achieved throughput under varying network conditions, reduces the server
cost by up to 6.8x and improves accuracy by 20.7% under latency constraints,
while providing robust operation under uncertain connectivity conditions and
significant energy savings compared to cloud-centric execution.Comment: Accepted at the 26th Annual International Conference on Mobile
Computing and Networking (MobiCom), 202
Power Consumption Analysis, Measurement, Management, and Issues:A State-of-the-Art Review of Smartphone Battery and Energy Usage
The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed
Trustworthy Wireless Personal Area Networks
In the Internet of Things (IoT), everyday objects are equipped with the ability to compute and communicate. These smart things have invaded the lives of everyday people, being constantly carried or worn on our bodies, and entering into our homes, our healthcare, and beyond. This has given rise to wireless networks of smart, connected, always-on, personal things that are constantly around us, and have unfettered access to our most personal data as well as all of the other devices that we own and encounter throughout our day. It should, therefore, come as no surprise that our personal devices and data are frequent targets of ever-present threats. Securing these devices and networks, however, is challenging. In this dissertation, we outline three critical problems in the context of Wireless Personal Area Networks (WPANs) and present our solutions to these problems.
First, I present our Trusted I/O solution (BASTION-SGX) for protecting sensitive user data transferred between wirelessly connected (Bluetooth) devices. This work shows how in-transit data can be protected from privileged threats, such as a compromised OS, on commodity systems. I present insights into the Bluetooth architecture, Intel’s Software Guard Extensions (SGX), and how a Trusted I/O solution can be engineered on commodity devices equipped with SGX.
Second, I present our work on AMULET and how we successfully built a wearable health hub that can run multiple health applications, provide strong security properties, and operate on a single charge for weeks or even months at a time. I present the design and evaluation of our highly efficient event-driven programming model, the design of our low-power operating system, and developer tools for profiling ultra-low-power applications at compile time.
Third, I present a new approach (VIA) that helps devices at the center of WPANs (e.g., smartphones) to verify the authenticity of interactions with other devices. This work builds on past work in anomaly detection techniques and shows how these techniques can be applied to Bluetooth network traffic. Specifically, we show how to create normality models based on fine- and course-grained insights from network traffic, which can be used to verify the authenticity of future interactions
HAPI: Hardware-Aware Progressive Inference
Convolutional neural networks (CNNs) have recently become the
state-of-the-art in a diversity of AI tasks. Despite their popularity, CNN
inference still comes at a high computational cost. A growing body of work aims
to alleviate this by exploiting the difference in the classification difficulty
among samples and early-exiting at different stages of the network.
Nevertheless, existing studies on early exiting have primarily focused on the
training scheme, without considering the use-case requirements or the
deployment platform. This work presents HAPI, a novel methodology for
generating high-performance early-exit networks by co-optimising the placement
of intermediate exits together with the early-exit strategy at inference time.
Furthermore, we propose an efficient design space exploration algorithm which
enables the faster traversal of a large number of alternative architectures and
generates the highest-performing design, tailored to the use-case requirements
and target hardware. Quantitative evaluation shows that our system consistently
outperforms alternative search mechanisms and state-of-the-art early-exit
schemes across various latency budgets. Moreover, it pushes further the
performance of highly optimised hand-crafted early-exit CNNs, delivering up to
5.11x speedup over lightweight models on imposed latency-driven SLAs for
embedded devices.Comment: Accepted at the 39th International Conference on Computer-Aided
Design (ICCAD), 202
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