Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users

Design, analysis, tools and applications for programmable high-speed and power-aware 4G processors

Data rate traffic and communication capacity demand have been increased continuously. Therefore, a highly advanced 4G wireless system is required to meet a high demand for modern mobile terminals. For getting a further improvement for 4G communication systems, new paradigms of design, analysis tools and applications for 4G communication processors are necessary. In this paper, some of these new paradigms are discussed. Furthermore, a single-step discrete cosine transform truncation (DCTT) method is proposed for the modeling-simulation in signal integrity verification for high-speed communication processors. ©2011 IEEE.published_or_final_versio

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies. However, the increasingly complex nature of these neural networks means that they are particularly suited for server computers with powerful GPUs. We envision that deep learning applications will be eventually and widely deployed on mobile devices, e.g., smartphones, self-driving cars, and drones. Therefore, in this paper, we aim to understand the resource requirements (time, memory) of CNNs on mobile devices. First, by deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze the performance and resource usage for every layer of the CNNs. Our findings point out the potential ways of optimizing the performance on mobile devices. Second, we model the resource requirements of the different CNN computations. Finally, based on the measurement, pro ling, and modeling, we build and evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor) as the input and estimates the compute time and resource usage of the CNN, to give insights about whether and how e ciently a CNN can be run on a given mobile platform. In doing so Augur tackles several challenges: (i) how to overcome pro ling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations

A review on mobile operating systems and application development platforms

The previous existing mobile technologies were only limited to voice and short messages, organized between several network operators and service providers. However, recent advancements in technologies, introduction, and development of the smartphones added many features such: high-speed processors, huge memory, multitasking, screens with large-resolution, utile communication hardware, and so on. Mobile devices were evolving into general-purpose computers, which resulted in the development of various technological platforms, operating systems, and platforms for the development of the applications. All these results in the occurrence of various competitive offers on the market. The above-mentioned features, processing speed and applications available on mobile devices are affected by underlying operating systems. In this paper, there will be discussed the mobile operating systems and application development platforms.&nbsp

DLP acceleration on general purpose cores

High-performance and power-efficient multimedia computing drives the design of modern and increasingly utilized mobile devices. State-of-the-art low power processors already utilize chip multiprocessors (CMP) that add dedicated DLP accelerators for emerging multimedia applications and 3D games. Such heterogeneous processors deliver desired performance and efficiency at the cost of extra hardware specialized accelerators. In this paper, we propose dynamically-tuned vector execution (DVX) by morphing one or more available cores in a CMP into a DLP accelerator. DVX improves performance and power efficiency of the CMP, without additional costs for dedicated accelerators

E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a key technology for emerging applications such as automatic speech recognition, machine translation or image description. Long Short Term Memory (LSTM) networks are the most successful RNN implementation, as they can learn long term dependencies to achieve high accuracy. Unfortunately, the recurrent nature of LSTM networks significantly constrains the amount of parallelism and, hence, multicore CPUs and many-core GPUs exhibit poor efficiency for RNN inference. In this paper, we present E-PUR, an energy-efficient processing unit tailored to the requirements of LSTM computation. The main goal of E-PUR is to support large recurrent neural networks for low-power mobile devices. E-PUR provides an efficient hardware implementation of LSTM networks that is flexible to support diverse applications. One of its main novelties is a technique that we call Maximizing Weight Locality (MWL), which improves the temporal locality of the memory accesses for fetching the synaptic weights, reducing the memory requirements by a large extent. Our experimental results show that E-PUR achieves real-time performance for different LSTM networks, while reducing energy consumption by orders of magnitude with respect to general-purpose processors and GPUs, and it requires a very small chip area. Compared to a modern mobile SoC, an NVIDIA Tegra X1, E-PUR provides an average energy reduction of 92x