737 research outputs found
Energy challenges for ICT
The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT
๊ธฐ๊ธฐ ์์์์ ์ฌ์ธต ์ ๊ฒฝ๋ง ๊ฐ์ธํ ๋ฐฉ๋ฒ
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ, 2019. 2. Egger, Bernhard.There exist several deep neural network (DNN) architectures suitable for embedded inference, however little work has focused on training neural networks on-device.
User customization of DNNs is desirable due to the difficulty of collecting a training set representative of real world scenarios.
Additionally, inter-user variation means that a general model has a limitation on its achievable accuracy.
In this thesis, a DNN architecture that allows for low power on-device user customization is proposed.
This approach is applied to handwritten character recognition of both the Latin and the Korean alphabets.
Experiments show a 3.5-fold reduction of the prediction error after user customization for both alphabets compared to a DNN trained with general data.
This architecture is additionally evaluated using a number of embedded processors demonstrating its practical application.๋ด์ฅํ ๊ธฐ๊ธฐ์์ ์ฌ์ธต ์ ๊ฒฝ๋ง์ ์ถ๋ก ํ ์ ์๋ ์ํคํ
์ฒ๋ค์ ์กด์ฌํ์ง๋ง ๋ด์ฅํ ๊ธฐ๊ธฐ์์ ์ ๊ฒฝ๋ง์ ํ์ตํ๋ ์ฐ๊ตฌ๋ ๋ณ๋ก ์ด๋ค์ง์ง ์์๋ค. ์ค์ ํ๊ฒฝ์ ๋ฐ์ํ๋ ํ์ต์ฉ ๋ฐ์ดํฐ ์งํฉ์ ๋ชจ์ผ๋ ๊ฒ์ด ์ด๋ ต๊ณ ์ฌ์ฉ์๊ฐ์ ๋ค์์ฑ์ผ๋ก ์ธํด ์ผ๋ฐ์ ์ผ๋ก ํ์ต๋ ๋ชจ๋ธ์ด ์ถฉ๋ถํ ์ ํ๋๋ฅผ ๊ฐ์ง๊ธฐ์ ํ๊ณ๊ฐ ์กด์ฌํ๊ธฐ ๋๋ฌธ์ ์ฌ์ฉ์ ๋ง์ถคํ ์ฌ์ธต ์ ๊ฒฝ๋ง์ด ํ์ํ๋ค. ์ด ๋
ผ๋ฌธ์์๋ ๊ธฐ๊ธฐ์์์ ์ ์ ๋ ฅ์ผ๋ก ์ฌ์ฉ์ ๋ง์ถคํ๊ฐ ๊ฐ๋ฅํ ์ฌ์ธต ์ ๊ฒฝ๋ง ์ํคํ
์ฒ๋ฅผ ์ ์ํ๋ค. ์ด๋ฌํ ์ ๊ทผ ๋ฐฉ๋ฒ์ ๋ผํด์ด์ ํ๊ธ์ ํ๊ธฐ์ฒด ๊ธ์ ์ธ์์ ์ ์ฉ๋๋ค. ๋ผํด์ด์ ํ๊ธ์ ์ฌ์ฉ์ ๋ง์ถคํ๋ฅผ ์ ์ฉํ์ฌ ์ผ๋ฐ์ ์ธ ๋ฐ์ดํฐ๋ก ํ์ตํ ์ฌ์ธต ์ ๊ฒฝ๋ง๋ณด๋ค 3.5๋ฐฐ๋ ์์ ์์ธก ์ค๋ฅ์ ๊ฒฐ๊ณผ๋ฅผ ์ป์๋ค. ๋ํ ์ด ์ํคํ
์ฒ์ ์ค์ฉ์ฑ์ ๋ณด์ฌ์ฃผ๊ธฐ ์ํ์ฌ ๋ค์ํ ๋ด์ฅํ ํ๋ก์ธ์์์ ์คํ์ ์งํํ์๋ค.Abstract i
Contents iii
List of Figures vii
List of Tables ix
Chapter 1 Introduction 1
Chapter 2 Motivation 4
Chapter 3 Background 6
3.1 Deep Neural Networks 6
3.1.1 Inference 6
3.1.2 Training 7
3.2 Convolutional Neural Networks 8
3.3 On-Device Acceleration 9
3.3.1 Hardware Accelerators 9
3.3.2 Software Optimization 10
Chapter 4 Methodology 12
4.1 Initialization 13
4.2 On-Device Training 14
Chapter 5 Implementation 16
5.1 Pre-processing 16
5.2 Latin Handwritten Character Recognition 17
5.2.1 Dataset and BIE Selection 17
5.2.2 AE Design 17
5.3 Korean Handwritten Character Recognition 21
5.3.1 Dataset and BIE Selection 21
5.3.2 AE Design 21
Chapter 6 On-Device Acceleration 26
6.1 Architecure Optimizations 27
6.2 Compiler Optimizations 29
Chapter 7 Experimental Setup 30
Chapter 8 Evaluation 33
8.1 Latin Handwritten Character Recognition 33
8.2 Korean Handwritten Character Recognition 38
8.3 On-Device Acceleration 40
Chapter 9 Related Work 44
Chapter 10 Conclusion 47
Bibliography 47
์์ฝ 55
Acknowledgements 56Maste
A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems
Recent technological advances have greatly improved the performance and
features of embedded systems. With the number of just mobile devices now
reaching nearly equal to the population of earth, embedded systems have truly
become ubiquitous. These trends, however, have also made the task of managing
their power consumption extremely challenging. In recent years, several
techniques have been proposed to address this issue. In this paper, we survey
the techniques for managing power consumption of embedded systems. We discuss
the need of power management and provide a classification of the techniques on
several important parameters to highlight their similarities and differences.
This paper is intended to help the researchers and application-developers in
gaining insights into the working of power management techniques and designing
even more efficient high-performance embedded systems of tomorrow
Dynamic Power Management for Neuromorphic Many-Core Systems
This work presents a dynamic power management architecture for neuromorphic
many core systems such as SpiNNaker. A fast dynamic voltage and frequency
scaling (DVFS) technique is presented which allows the processing elements (PE)
to change their supply voltage and clock frequency individually and
autonomously within less than 100 ns. This is employed by the neuromorphic
simulation software flow, which defines the performance level (PL) of the PE
based on the actual workload within each simulation cycle. A test chip in 28 nm
SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled
from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct
PLs. By measurement of three neuromorphic benchmarks it is shown that the total
PE power consumption can be reduced by 75%, with 80% baseline power reduction
and a 50% reduction of energy per neuron and synapse computation, all while
maintaining temporary peak system performance to achieve biological real-time
operation of the system. A numerical model of this power management model is
derived which allows DVFS architecture exploration for neuromorphics. The
proposed technique is to be used for the second generation SpiNNaker
neuromorphic many core system
RISC-V-Based Platforms forย HPC: Analyzing Non-functional Properties forย Future HPC andย Big-Data Clusters
High-Performance Computing (HPC) have evolved to be used to perform simulations of systems where physical experimentation is prohibitively impractical, expensive, or dangerous. This paper provides a general overview and showcases the analysis of non-functional properties
in RISC-V-based platforms for HPCs. In particular, our analyses target the evaluation of power and energy control, thermal management, and reliability assessment of promising systems, structures, and technologies devised for current and future generation of HPC machines. The main set of design methodologies and technologies developed within the activities of the Future and HPC & Big Data spoke of the National Centre of HPC, Big Data and Quantum Computing project are described along with the description of the testbed for experimenting two-phase cooling approaches
Multiprocessor System-on-Chips based Wireless Sensor Network Energy Optimization
Wireless Sensor Network (WSN) is an integrated part of the Internet-of-Things (IoT) used to monitor the physical or environmental conditions without human intervention. In WSN one of the major challenges is energy consumption reduction both at the sensor nodes and network levels. High energy consumption not only causes an increased carbon footprint but also limits the lifetime (LT) of the network. Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) are becoming the de-facto computing platform for computationally extensive real-time applications in IoT due to their high performance and exceptional quality-of-service. In this thesis a task scheduling problem is investigated using MPSoCs architecture for tasks with precedence and deadline constraints in order to minimize the processing energy consumption while guaranteeing the timing constraints. Moreover, energy-aware nodes clustering is also performed to reduce the transmission energy consumption of the sensor nodes. Three distinct problems for energy optimization are investigated given as follows:
First, a contention-aware energy-efficient static scheduling using NoC based heterogeneous MPSoC is performed for real-time tasks with an individual deadline and precedence constraints. An offline meta-heuristic based contention-aware energy-efficient task scheduling is developed that performs task ordering, mapping, and voltage assignment in an integrated manner. Compared to state-of-the-art scheduling our proposed algorithm significantly improves the energy-efficiency.
Second, an energy-aware scheduling is investigated for a set of tasks with precedence constraints deploying Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs. A novel population based algorithm called ARSH-FATI is developed that can dynamically switch between explorative and exploitative search modes at run-time. ARSH-FATI performance is superior to the existing task schedulers developed for homogeneous VFI-NoC-MPSoCs.
Third, the transmission energy consumption of the sensor nodes in WSN is reduced by developing ARSH-FATI based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called Novel Ranked Based Clustering (NRC). In cluster formation parameters such as residual energy, distance parameters, and workload on CHs are considered to improve LT of the network. The results prove that ARSH-FATI-CHS outperforms other state-of-the-art clustering algorithms in terms of LT.University of Derby, Derby, U
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