Mobile Computing

There are billions of mobile devices across the planet. They come in many shapes, sizes and capabilities. The picture below shows the state of smartphone computing as of a few years ago. The random assortment of rectangles represents the rich diversity in Android devices.

Android device fragmentation

Despite the rich proliferation of mobile devices, all mobile devices struggle to deliver high computational capability because they are limited by battery density and capacity, and stringent thermal envelopes. The group's mobile computing research effort focuses on tackling these hard problems involving holistic mobile system design. We develop high-performance mobile computing systems, the kind that can usher in next-generation applications such as perceptual computing and augmented reality. But the buck does not stop there. Using mobile devices also exposes us to security issues. Mobile malware is a persistent problem that is widespread. So, we also study how to build secure systems that do not compromise system efficiency.

The research takes an integrated look at the whole mobile computing stack, from the application user down to the hardware. The solution space that we explore involves (1) tuning the software stack to utilize existing hardware better; (2) designing future hardware with the software and algorithms in mind; and (3) taking into account user quality of experience (QoE).

Select Publications

M. Hill and V. J. Reddi, “Accelerator-Level Parallelism (ALP),” arXiv, vol. arXiv:1907.02064 [cs.DC], 2019.Abstract

With the slowing of technology scaling, the only known way to further improve computer system performance under energy constraints is to employ hardware ​accelerators​. Already today, many chips in m​ obile, edge and cloud computing concurrently employ multiple accelerators in what we call ​accelerator-level parallelism (ALP)​. For the needed benefits of ALP to spread to computer systems more broadly, we herein charge the community to develop better “best practices” for: targeting accelerators, managing accelerator concurrency, choreographing inter-accelerator communication, and productively programming accelerators.

M. Hill and V. J. Reddi, “Gables: A Roofline Model for Mobile SoCs,” in Proceedings of the 25th International Symposium on High Performance Computer Architecture (HPCA), 2019.Abstract

Over a billion mobile consumer system-on-chip (SoC) chipsets ship each year. Of these, the mobile consumer market undoubtedly involving smartphones has a significant market share. Most modern smartphones comprise of advanced SoC architectures that are made up of multiple cores, GPS, and many different programmable and fixed-function accelerators connected via a complex hierarchy of interconnects with the goal of running a dozen or more critical software usecases under strict power, thermal and energy constraints. The steadily growing complexity of a modern SoC challenges hardware computer architects on how best to do early stage ideation. Late SoC design typically relies on detailed full-system simulation once the hardware is specified and accelerator software is written or ported. However, early-stage SoC design must often select accelerators before a single line of software is written. To help frame SoC thinking and guide early stage mobile SoC design, in this paper we contribute the Gables model that refines and retargets the Roofline model—designed originally for the performance and bandwidth limits of a multicore chip—to model each accelerator on a SoC, to apportion work concurrently among different accelerators (justified by our usecase analysis), and calculate a SoC performance upper bound. We evaluate the Gables model with an existing SoC and develop several extensions that allow Gables to inform early stage mobile SoC design.

Index Terms—Accelerator architectures, Mobile computing, Processor architecture, System-on-Chip

V. J. Reddi, H. Yoon, and A. Knies, “Two Billion Devices and Counting,” IEEE Micro, vol. 38, no. 1, pp. 6–21, 2018. Publisher's VersionAbstract

Mobile computing has grown drastically over the past decade. Despite the rapid pace of advancements, mobile device understanding, benchmarking, and evaluation are still in their infancies, both in industry and academia. This article presents an industry perspective on the challenges facing mobile computer architecture, specifically involving mobile workloads, benchmarking, and experimental methodology, with the hope of fostering new research within the community to address pending problems. These challenges pose a threat to the systematic development of future mobile systems, which, if addressed, can elevate the entire mobile ecosystem to the next level.

Mobile devices have come a long way from the first portable cellular phone developed by Motorola in 1973. Most modern smartphones are good enough to replace desktop computers. A smartphone today has enough computing power to be on par with the fastest supercomputers from the 1990s.

For instance, the Qualcomm Adreno 540 GPU found in the latest smartphones has a peak compute capability of more than 500 Gflops, putting it in competition with supercomputers that were on the TOP500 list in the early to mid-1990s. Mobile computing has experienced an unparalleled level of growth over the past decade. At the time of this writing, there are more than 2 billion mobile devices in the world.1 But perhaps even more importantly, mobile phones are showing no signs of slowing in uptake. In fact, smartphone adoption rates are on the rise. The number of devices is rising as mobile device penetration increases in markets like India and China. It is anticipated that the number of mobile subscribers will grow past 6 billion in the coming years.2 As Figure 1 shows, while the Western European and North American markets are reaching saturation, the vast majority of growth is coming from countries in Asia. Given that only 35 percent of the world’s population has thus far adopted mobile technology, there is still significant room for growth and innovation.

Y. Zhu and V. J. Reddi, “GreenWeb: Language Extensions for Energy-Efficient Mobile Web Computing,” in Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2016, vol. 51, no. 6, pp. 145-160. Publisher's VersionAbstract

Web computing is gradually shifting toward mobile devices, in which the energy budget is severely constrained. As a result, Web developers must be conscious of energy efficiency. However, current Web languages provide developers little control over energy consumption. In this paper, we take a first step toward language-level research to enable energy-efficient Web computing. Our key motivation is that mobile systems can wisely budget energy usage if informed with user quality-of-service (QoS) constraints. To do this, programmers need new abstractions. We propose two language abstractions, QoS type and QoS target, to capture two fundamental aspects of user QoS experience. We then present GreenWeb, a set of language extensions that empower developers to easily express the QoS abstractions as program annotations. As a proof of concept, we develop a GreenWeb runtime, which intelligently determines how to deliver specified user QoS expectation while minimizing energy consumption. Overall, GreenWeb shows significant energy savings (29.2% ⇠ 66.0%) over Android’s default Interactive governor with few QoS violations. Our work demonstrates a promising first step toward language innovations for energy-efficient Web computing. Categories and Subject Descriptors D.3.2 [Programming Language]: Language Classifications–Specialized application languages; D.3.3 [Programming Language]: Language Constructs and Features–Constraints Keywords Energy-efficiency, Web, Mobile computing

M. Halpern, Y. Zhu, and V. J. Reddi, “Mobile Cpu's Rise to Power: Quantifying the Impact of Generational Mobile Cpu Design Trends on Performance, Energy, and User Satisfaction,” in High Performance Computer Architecture (HPCA), 2016 IEEE International Symposium on, 2016, pp. 64–76. Publisher's VersionAbstract

In this paper, we assess the past, present, and future of mobile CPU design. We study how mobile CPU designs trends have impacted the end-user, hardware design, and the holistic mobile device. We analyze the evolution of ten cutting-edge mobile CPU designs released over the past seven years. Specifically, we report measured performance, power, energy and user satisfaction trends across mobile CPU generations. A key contribution of our work is that we contextualize the mobile CPU’s evolution in terms of user satisfaction, which has largely been absent from prior mobile hardware studies. To bridge the gap between mobile CPU design and user satisfaction, we construct and conduct a novel crowdsourcing study that spans over 25,000 survey participants using the Amazon Mechanical Turk service. Our methodology allows us to identify what mobile CPU design techniques provide the most benefit to the end-user’s quality of user experience. Our results quantitatively demonstrate that CPUs play a crucial role in modern mobile system-on-chips (SoCs). Over the last seven years, both single- and multicore performance improvements have contributed to end-user satisfaction by reducing user-critical application response latencies. Mobile CPUs aggressively adopted many power-hungry desktoporiented design techniques to reach these performance levels. Unlike other smartphone components (e.g. display and radio) whose peak power consumption has decreased over time, the mobile CPU’s peak power consumption has steadily increased. As the limits of technology scaling restrict the ability of desktop-like scaling to continue for mobile CPUs, specialized accelerators appear to be a promising alternative that can help sustain the power, performance, and energy improvements that mobile computing necessitates. Such a paradigm shift will redefine the role of the CPU within future SoCs, which merit several design considerations based on our findings.

M. Kazdagli, L. Huang, V. J. Reddi, and M. Tiwari, “EMMA: A New Platform to Evaluate Hardware-based Mobile Malware Analyses,” arXiv preprint arXiv:1603.03086, 2016.Abstract

Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy computing platforms, especially mobile platforms. Quantifying the efficacy of HMDs against malicious adversaries is thus an important problem. The challenge lies in that real-world malware typically adapts to defenses, evades being run in experimental settings, and hides behind benign applications. Thus, realizing the potential of HMDs as a line of defense – that has a small and battery-efficient code base – requires a rigorous foundation for evaluating HMDs. To this end, we introduce EMMA—a platform to evaluate the efficacy of HMDs for mobile platforms. EMMA deconstructs malware into atomic, orthogonal actions and introduces a systematic way of pitting different HMDs against a diverse subset of malware hidden inside benign applications. EMMA drives both malware and benign programs with real user-inputs to yield an HMD’s effective operating range— i.e., the malware actions a particular HMD is capable of detecting. We show that small atomic actions, such as stealing a Contact or SMS, have surprisingly large hardware footprints, and use this insight to design HMD algorithms that are less intrusive than prior work and yet perform 24.7% better. Finally, EMMA brings up a surprising new result— obfuscation techniques used by malware to evade static analyses makes them more detectable using HMDs.

Y. Zhu and V. J. Reddi, “WebCore: Architectural Support for Mobile Web Browsing,” Proceedings of the 41st International Symposium on Computer Architecture (ISCA), vol. 42, no. 3, pp. 541–552, 2014. Publisher's VersionAbstract

The Web browser is undoubtedly the single most important application in the mobile ecosystem. An average user spends 72 minutes each day using the mobile Web browser. Web browser internal engines (e.g., WebKit) are also growing in importance because they provide a common substrate for developing various mobile Web applications. In a user-driven, interactive, and latency-sensitive environment, the browser’s performance is crucial. However, the battery-constrained nature of mobile devices limits the performance that we can deliver for mobile Web browsing. As traditional general-purpose techniques to improve performance and energy efficiency fall short, we must employ domain-specific knowledge while still maintaining general-purpose flexibility.

In this paper, we first perform design-space exploration to identify appropriate general-purpose architectures that uniquely fit the characteristics of a popular Web browsing engine. Despite our best effort, we discover sources of energy inefficiency in these customized general-purpose architectures. To mitigate these inefficiencies, we propose, synthesize, and evaluate two new domain-specific specializations, called the Style Resolution Unit and the Browser Engine Cache. Our optimizations boost energy efficiency and at the same time improve mobile Web browsing performance. As emerging mobile workloads increasingly rely more on Web browser technologies, the type of optimizations we propose will become important in the future and are likely to have lasting widespread impact.

V. J. Reddi, B. Lee, T. Chilimbi, and K. Vaid, “Web Search Using Mobile Cores: Quantifying and Mitigating the Price of Efficiency,” in International Symposium on Computer Architecture, 2010. Publisher's VersionAbstract

The commoditization of hardware, data center economies of scale, and Internet-scale workload growth all demand greater power efficiency to sustain scalability. Traditional enterprise workloads, which are typically memory and I/O bound, have been well served by chip multiprocessors comprising of small, power-efficient cores. Recent advances in mobile computing have led to modern small cores capable of delivering even better power efficiency. While these cores can deliver performance-per-Watt efficiency for data center workloads, small cores impact application quality-of-service robustness, and flexibility, as these workloads increasingly invoke computationally intensive kernels. These challenges constitute the price of efficiency. We quantify efficiency for an industry-strength online web search engine in production at both the microarchitecture- and system-level, evaluating search on server and mobile-class architectures using Xeon and Atom processors.

Categories and Subject Descriptors

C.0 [Computer Systems Organization]: General—System architectures; C.4 [Computer Systems Organization]: Performance of Systems—Design studies, Reliability, availability, and serviceability

General Terms

Measurement, Experimentation, Performance

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