We are experiencing an explosive growth in the number of consumer devices, including smartphones, tablets, web-based computers such as Chromebooks, and wearable devices. For this class of devices, energy efficiency is a first-class concern due to the limited battery capacity and thermal power budget. We find that data movement is a major contributor to the total system energy and execution time in consumer devices. The energy and performance costs of moving data between the memory system and the compute units are significantly higher than the costs of computation. As a result, addressing data movement is crucial for consumer devices. In this work, we comprehensively analyze the energy and performance impact of data movement for several widely-used Google consumer workloads: (1) the Chrome web browser; (2) TensorFlow Mobile, Google's machine learning framework; (3) video playback, and (4) video capture, both of which are used in many video services such as YouTube and Google Hangouts. We find that processing-in-memory (PIM) can significantly reduce data movement for all of these workloads, by performing part of the computation close to memory. Each workload contains simple primitives and functions that contribute to a significant amount of the overall data movement. We investigate whether these primitives and functions are feasible to implement using PIM, given the limited area and power constraints of consumer devices. Our analysis shows that offloading these primitives to PIM logic, consisting of either simple cores or specialized accelerators, eliminates a large amount of data movement, and significantly reduces total system energy (by an average of 55.4% across the workloads) and execution time (by an average of 54.2%).

Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks

Future wireless networks will provide high-bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from virtual reality to the Internet of Things. To this aim, edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 improves the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bit-rate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined.

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Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between different color channels and the two-dimensional entropy are calculated. In the frequency domain, the statistical characteristics of the two-dimensional entropy and the mutual information of the filtered subband images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The implementation is available on github https://github.com/jacob6/ENIQA
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/pdf/no-reference-color-image-quality-assessment-from-entropy-to-565xd2jngs.pdf

No-reference color image quality assessment: from entropy to perceptual quality

Device-to-device content caching techniques in 5G: A taxonomy, solutions, and challenges

We are experiencing an explosive growth in the number of consumer devices, including smartphones, tablets, web-based computers such as Chromebooks, and wearable devices. For this class of devices, ...

Google Workloads for Consumer Devices

To implement caching devices in content delivery systems, flash memory is preferable to hard disk drives from the performance perspective. Nevertheless, the higher bit cost of flash memory is one major obstacle for the wide real-life deployment of flash-based video caching. This paper presents a set of design solutions to address this cost issue. First, we present a flash memory error tolerance design strategy customized for video data storage, which can enable the use of lower-cost less-reliable flash memory chips for video storage. The cost challenge can also be addressed by reducing the video storage footprint through on-the-fly transcoding. However, direct transcoding suffers from a high implementation cost. We propose two design techniques that can largely reduce the transcoding complexity at minimal storage overhead in flash memory. All the developed design solutions share the common feature of cohesively exploring the characteristics of video coding and flash memory device physics. Their effectiveness has been well demonstrated through experiments with 20-nm MLC NAND flash memory chips and extensive simulations with representative video sequences.

Realizing Low-Cost Flash Memory Based Video Caching in Content Delivery Systems

Motivated by the significant storage footprint of OS/Apps in mobile devices, this paper studies the realization of OS/Apps transparent compression. In spite of its obvious advantage, this feature however is not widely available in commercial mobile devices, which is due to the justifiable concern on the read latency penalty. In conventional practice on implementing transparent compression, read latency overhead comes from two aspects, including read amplification and decompression computational latency. This paper presents simple yet effective design solutions to eliminate the read amplification at the filesystem level and eliminate the computational latency overhead at the computer architecture level. To demonstrate its practical feasibility, we first implemented a prototyping filesystem to empirically verify the realization of transparent compression with zero read amplification. We further demonstrated that the OS/Apps footprint can be reduced by up to 39 percent on a Nexus 7 tablet installed with Android 5.0. Through application-specific integrated circuit (ASIC) synthesis, we show that the proposed computer architecture level design solution can eliminate the decompression latency overhead with very small silicon cost.

Danni Xiong

Papers

Realizing Low-Cost Flash Memory Based Video Caching in Content Delivery Systems

Realizing Transparent OS/Apps Compression in Mobile Devices at Zero Latency Overhead