Optimization Tools for ConvNets on the Edge

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TVFS: Topology Voltage Frequency Scaling for Reliable Embedded ConvNets

This brief introduces Topology Voltage Frequency Scaling (TVFS), a performance management technique for embedded Convolutional Neural Networks (ConvNets) deployed on low-power CPUs. Using TVFS, pre-trained ConvNets can be efficiently processed over a continuous stream of data, enabling reliable and predictable multi-inference tasks under latency constraints. Experimental results, collected from an image classification task built with MobileNet-v1 and ported into an ARM Cortex-A15 core, reveal TVFS holds fast and continuous inference (from few runs, up to 2000), ensuring a limited accuracy loss (from 0.9% to 3.1%), and better thermal profiles (average temperature 16.4 °C below the on-chip critical threshold)

Enabling DVFS Side-Channel Attacks for Neural Network Fingerprinting in Edge Inference Services

The Inference-as-a-Service (IaaS) delivery model provides users access to pre-trained deep neural networks while safeguarding network code and weights. However, IaaS is not immune to security threats, like side-channel attacks (SCAs), that exploit unintended information leakage from the physical characteristics of the target device. Exposure to such threats grows when IaaS is deployed on distributed computing nodes at the edge. This work identifies a potential vulnerability of low-power CPUs that facilitates stealing the deep neural network architecture without physical access to the hardware or interference with the execution flow. Our approach relies on a Dynamic Voltage and Frequency Scaling (DVFS) side-channel attack, which monitors the CPU frequency state during the inference stages. Specifically, we introduce a dedicated load-testing methodology that imprints distinguishable signatures of the network on the frequency traces. A machine learning classifier is then used to infer the victim architecture. Experimental results on two commercial ARM Cortex-A CPUs, the A72 and A57, demonstrate the attack can identify the target architecture from a pool of 12 convolutional neural networks with an average accuracy of 98.7% and 92.4

Optimality Assessment of Memory-Bounded ConvNets Deployed on Resource-Constrained RISC Cores

A cost-effective implementation of Convolutional Neural Nets on the mobile edge of the Internet-of-Things (IoT) requires smart optimizations to fit large models into memory-constrained cores. Reduction methods that use a joint combination of filter pruning and weight quantization have proven efficient in searching the compression that ensures minimum model size without accuracy loss. However, there exist other optimal configurations that stem from the memory constraint. The objective of this work is to make an assessment of such memory-bounded implementations and to show that most of them are centred on specific parameter settings that are found difficult to be implemented on a low-power RISC. Hence, the focus is on quantifying the distance to optimality of the closest implementations that instead can be actually deployed on hardware. The analysis is powered by a two-stage framework that efficiently explores the memory-accuracy space using a lightweight, hardware-conscious heuristic optimization. Results are collected from three realistic IoT tasks (Image Classification on CIFAR-10, Keyword Spotting on the Speech Commands Dataset, Facial Expression Recognition on Fer2013) run on RISC cores (Cortex-M by ARM) with few hundreds KB of on-chip RAM

Pulse-induced switches in a Josephson tunnel stacked device

Pulse activated transitions from the metastable to the running state and viceversa have been observed in a stacked double tunnel Nb-based Josephson system. Experimental results are compared with numerical simulations based on the Sine-Gordon model of the stacked junctions by injecting pulses with variable amplitude in one of the junctions of the stack, and observing the voltage response of the other junction. Both experimental and numerical results show the possibility to induce both direct and back-switching transitions from the metastable to the running state simply by changing the amplitude of the electronic pulses injected across the stack device.Comment: Submitted to Appl.Phys.Letters, May 2001 PDF format: 14 pages, 3 Figure

Monitoring of cfrp-strengthened reinforced concrete bridge spans in low temperature conditions

The article discusses strengthening bridges using composite materials at extreme low temperatures. Provides the results some experimental studies FRP strengthened concrete samples at low temperatures

Разработка и формализация корпоративной стратегии предприятия

Выпускная квалификационная работа содержит 110 стр., 19 таблиц, 15 рисунков, 30 использованных источников, 2 приложения. Ключевые слова: корпоративная стратегия, SWOT-анализ, матрица Маккински, матрица Томпсона и Стрикленда, модель Артур де Литтл, модель «5 сил Портера», ключевые показатели эффективности, система управления по показателям, корпоративная социальная ответственность. Объектом исследования является корпоративная стратегия ОАО «ТЭМЗ». Целью дипломной работы является рассмотрение проблем разработки и формализации корпоративной стратегии предприятия. В процессе исследования использованы законодательные и методические материалы, учебные пособия, публикации в специальных журналах, связанные с вопросами корпоративного управления. В результате исследования была осуществлена разработка и формализация корпоративной стратегии ОАО «ТЭМЗ». Основные конструктивные, технологические и технико-эксплуатационные характеристики: введение раскрывает актуальность, цель исследования, теоретическую и практическую значимость работы, обосновывается выбор объекта и предмета исследования. В первой главе раскрыты теоретические основы разработки корпоративной стратегии. Во второй главе дана краткая характеристика предприятия, проведен анализ системы корпоративного управления на предприятии ОАО «ТЭМЗ». В третьей главе рассмотрен процесс разработки и формализации стратегии управления для ОАО «ТЭМЗ». Заключение содержит анализ результатов теоретических и экспериментальных исследований работы. Степень внедрения: одна из предложенных в результате разработки и формализации корпоративных стратегий уже принята на ОАО «ТЭМЗ» и включена в соответствующие разделы инвестиционного бизнес-плана. Область применения: полученные результаты разработки и формализации корпоративной стратегии, эффективности социальной ответственности управления могут быть использованы в управленческой работе ОАО «ТЭМЗ»». Экономическая эффективность/значимость работы. Разработанные и формализованные корпоративные стратегии позволят ОАО «ТЭМЗ» повысить производительность труда, уменьшить текучесть кадрового потенциала и производственный травматизм.Final qualifying work contains 110 pages, 19 tables, 15 figures, 30 of the used sources, 2 appendices. Key words: corporate strategy, SWOT analysis, matrix o Machinski, matrix Thompson and Strickland model Arthur de little, model "5 forces of porter", key performance indicators, control system indicators, corporate social responsibility. The object of study is the corporate strategy of JSC "TEMZ". The aim of the thesis is to examine the problems of the development and formalization of corporate strategy. In the process of the study used legislative and methodical materials, textbooks, publications in professional journals related to issues of corporate governance. The study was carried out to develop and formalization of the corporate strategy of JSC "TEMZ". The basic constructive, technological and technical-operational characteristics: the introduction reveals the relevance, research objective, theoretical and practical significance of the research, justify the choice of object and subject of research. The first Chapter describes theoretical basis of development of corporate strategy. The second Chapter gives a brief description of the enterprise, the analysis of the system of corporate management of JSC "TEMZ". The third Chapter describes the development process and the formalization of the strategy management for JSC "TEMZ". The conclusion contains an analysis of the results of theoretical and experimental research work. Degree of implementation: one of the proposed in the development and formalization of corporate strategies already adopted at JSC "TEMZ" and included in relevant sections of the investment business plan. Application field: the results of the development and formalization of corporate strategy, efficiency, social responsibility management can be used in managerial work of JSC "TEMZ". Economic efficiency and significance of the work. Developed and formalized corporate strategy will allow JSC "TEMZ" to increase productivity, to decrease the fluidity of human resources and industrial injuries

Performance Profiling of Embedded ConvNets under Thermal-Aware DVFS

Convolutional Neural Networks (ConvNets) can be shrunk to fit embedded CPUs adopted on mobile end-nodes, like smartphones or drones. The deployment onto such devices encompasses several algorithmic level optimizations, e.g., topology restructuring, pruning, and quantization, that reduce the complexity of the network, ensuring less resource usage and hence higher speed. Several studies revealed remarkable performance, paving the way towards real-time inference on low power cores. However, continuous execution at maximum speed is quite unrealistic due to a fast increase of the on-chip temperature. Indeed, proper thermal management is paramount to guarantee silicon reliability and a safe user experience. Power management schemes, like voltage lowering and frequency scaling, are common knobs to control the thermal stability. Obviously, this implies a performance degradation, often not considered during the training and optimization stages. The objective of this work is to present the performance assessment of embedded ConvNets under thermal management. Our study covers the behavior of two control policies, namely reactive and proactive, implemented through the Dynamic Voltage-Frequency Scaling (DVFS) mechanism available on commercial embedded CPUs. As benchmarks, we used four state-of-the-art ConvNets for computer vision flashed into the ARM Cortex-A15 CPU. With the collected results, we aim to show the existing temperature-performance trade-off and give a more realistic analysis of the maximum performance achievable. Moreover, we empirically demonstrate the strict relationship between the on-chip thermal behavior and the hyper-parameters of the ConvNet, revealing optimization margins for a thermal-aware design of neural network layers