In embedded processors, the RAM resources required by branch predictor compared to desktop or server processors are far from being reached. Utilizing the limited resources to design superior performance branch predictor has become urgent challenge. In this paper, we exploit the performance of complex TAGE implemented in ultra-small RAM processor. We first define design space exploration problem of the TAGE under the constraints of given RAM size and maximum global history register length. Then, based on the trace-driven simulation, the improved Particle Swarm Optimization algorithm is used to efficiently explore the specific parameters, rewarding design parameters with high prediction accuracy under RAM ranging from 0.125KB to 4KB. We found that, for the traces of this paper, the parameters under the 1.5KB RAM explored by our algorithm can achieve adequate accuracy. The performance loss is considerably small if we reduce the RAM resources from 8KB to 1.5KB. In addition, the misprediction rate of 1.5KB TAGE are reduced by 63.41% compared to 1.5KB Bi-mode. And, 0.25KB TAGE has almost the same accuracy with 4KB GShare.

Branch predictors are important components that affect the performance of modern microprocessors. Many of the existing prediction simulation platforms either only consider accuracy computed with coarse-grained updating model, or just are the low speed full system simulators. In this paper, We present SimpleBP, a lightweight prediction simulator based on trace driven. It leverages the SystemC language to simulate branch predictor at clock cycle granularity. Also, the CACTI tools is integrated to evaluate area and power consumption. Using SimpleBP, we first make hardware design analysis in a 64Kits storage budget with new added features(feedback delay, fetch width and RAM port number). The experimental results show that the prediction accuracy loss is less than 2% when the feedback delay increased. Under the same RAM area, when fetch width modified, various traces show different accuracy changes. When the area used for dual-port is saved to construct more entries of the subpredictors, the accuracy improvement is about 2.45%. Then, considering these new features, this paper does example optimization exploration of RAM size and subpredictors number through SimpleBP, and the results show many meaningful differences with the results simulated through other simulation platforms.

Thread migration is ubiquitous in multi-core architectures. When a thread migrates to a new core, the branch information of the branch predictor on new core is absent, which will lead to the predictor won't work as intended until the warm-up finish. In this poster, we point out that, when a thread migrates to a new core, the warm-up time of branch predictors can be reduced by migrating branch history information from the source core to the target. In addition, we improve branch prediction accuracy by migrating branch history information including prediction tables and GHR. Moreover, several migration strategies are introduced to fully exploit the performance of branch predictor migration. Preliminary performance results shows that, compared to the experiment baseline which dosen't migrate any branch history information, branch prediction migration reduces MPKI of the branch predictor on new core by 43.46% on average.

With the rapid proliferation of smartphones, hundreds of millions of mobile users are attracted to Instant Messaging (IM) apps. While such apps have brought convenience to our life, it comes with the price of great energy consumption, as these apps keep sending heartbeat messages to the server periodically in order to maintain an always-online connection. These frequent and fragmented transmissions result in a considerable amount of energy waste. In this paper, we investigate the "cost and potential of heartbeats". We quantify power consumption of heartbeats of real-world IM apps through extensive measurements. The measurement results confirm that huge power consumption is induced by heartbeats. The goal of this paper is to save energy by turning the energy wastage of heartbeats into transmitting useful data. Thus, we develop eTrain, a transmission management system running on Android phones, which takes advantage of IM heartbeats (as trains) to piggyback aggregated delay-tolerant apps' data such as e-mail and Weibo (as cargoes) via an online transmission strategy, so as to minimize the cumulative tail energy without sacrificing user-specified deadlines. Compared to other existing works, eTrain can reduce more energy consumption under the same settings. Experiments conducted on smartphones show that eTrain can achieve 12%-33% energy saving in various application scenarios.

Internet-enabled cameras pervade daily life, generating a huge amount of data, but most of the video they generate is transmitted over wires and analyzed offline with a human in the loop. The ubiquity of cameras limits the amount of video that can be sent to the cloud, especially on wireless networks where capacity is at a premium. In this paper, we present Vigil, a real-time distributed wireless surveillance system that leverages edge computing to support real-time tracking and surveillance in enterprise campuses, retail stores, and across smart cities. Vigil intelligently partitions video processing between edge computing nodes co-located with cameras and the cloud to save wireless capacity, which can then be dedicated to Wi-Fi hotspots, offsetting their cost. Novel video frame prioritization and traffic scheduling algorithms further optimize Vigil's bandwidth utilization. We have deployed Vigil across three sites in both whitespace and Wi-Fi networks. Depending on the level of activity in the scene, experimental results show that Vigil allows a video surveillance system to support a geographical area of coverage between five and 200 times greater than an approach that simply streams video over the wireless network. For a fixed region of coverage and bandwidth, Vigil outperforms the default equal throughput allocation strategy of Wi-Fi by delivering up to 25% more objects relevant to a user's query.

The present TV whitespace networks rely on spectrum occupancy databases to determine their operating channels. In this paper, we show that such databases cause non-negligible wastage of whitespace spectrum. We also report that whitespace channels can have very different quality due to interference from secondary devices and the leakage from TV broadcasts. Such disparity in channel quality is not captured by existing databases. We propose the use of spectrum measurements to overcome the above limitations of databases. In particular, we describe a system called V-Scope that leverages spectrum sensors on public vehicles to collect and report measurements from the road. These measurements are used as "anchor points" to construct various models to better determine whitespace spectrum, estimate its channel quality, and validate locations of primary and secondary devices.

We explore the use of TV whitespace communication systems for providing robust connectivity to vehicles. A key challenge in this setup is the asymmetry in transmit power limits -- the fixed base station is allowed to communicate at up to 4 W, while the mobile gateways in vehicles are limited to 100 mW. This paper presents a specific solution to deal with this asymmetry in which whitespace transceivers are used in the downlink direction while a more traditional cellular path is used in the uplink one. While heterogeneous communication systems have been considered before (e.g., in some satellite networks), our solution explores some unique opportunities that arise in vehicular systems. In particular, we describe a system called Scout that uses a front radio at the head of a vehicle to look ahead and identify the best channel parameters to be used when the rear radio eventually reaches the forward post. We use these channel estimates to adapt a number of transmission mechanisms for improving the performance of flows through this heterogeneous network. We have implemented and deployed this system on moving vehicles in an urban environment, and demonstrated 3 -- 8× performance improvement over simpler alternatives that do not use the scouting technique.

Unlicensed, secondary users of TV whitespaces today rely on spectrum occupancy databases to determine what spectrum they can use for their communication needs. In this paper, we first show that such spectrum databases (that depend solely on propagation models as per guidelines of the FCC in the USA) can be quite inaccurate leading to under-utilization of spectrum. Next, we propose that these spectrum databases can be significantly augmented using opportunistic measurements when possible. Instead of incorporating primary detection functions in each secondary device, we propose to use vehicle-mounted spectrum sensors that collect and report measurements from the road, which can serve as useful "anchor points" to enhance existing propagation models.

The recently released TV whitespaces offer a promising land for wireless communications. The secondary users of TV whitespaces today rely on spectrum occupancy databases to determine vacant TV channels for unlicensed communications. However, the accuracy of these databases (that depend solely on propagation models as per guidelines of the FCC) may be low. In this paper, we propose V-Scope - a vehicular sensing framework aimed to collect wide-area spectrum measurements for evaluating the accuracy of these databases. A key design feature of V-Scope is to leverage spectrum sensors mounted on public vehicles for collecting and reporting measurements from the road (opportunistic wardriving). We have currently deployed a version of our system on a single public transit bus traveling across a mid-sized city in the US. Based on measurements collected at over 1 million locations across a 100 square-km area, we find that databases tend to over-predict the coverage of certain TV broadcasts, unnecessarily blocking the usage of whitespace spectrum in a large area (up to 42% measured locations). We further propose ways of leveraging these measurements to enhance existing propagation models in databases.

We present Scout, a communication system leveraging TV whitespaces to support robust and high-speed streaming services. Scout uses two key techniques to improve video performance. First, it extends network coverage through an asymmetric network architecture where whitespace transceivers are used for the downlink direction while a cellular path is used for the uplink. Scout further leverages some unique opportunities that arise in vehicular systems. In particular, it sends a front radio to lookahead and identify the best channel parameters when the rear radio eventually reaches the forward post. We demonstrate the performance of Scout by using a single base station to stream a high-quality video to a vehicle driving along a 1.3km road stretch.

All practical wireless communication systems are prone to errors. At the symbol level, such wireless errors have a well-defined structure: When a receiver decodes a symbol erroneously, it is more likely that the decoded symbol is a good “approximation” of the transmitted symbol than a randomly chosen symbol among all possible transmitted symbols. Based on this property, we define approximate communication, a method that exploits this error structure to natively provide unequal error protection to data bits. Unlike traditional [forward error correction (FEC)-based] mechanisms of unequal error protection that consume additional network and spectrum resources to encode redundant data, the approximate communication technique achieves this property at the PHY layer without consuming any additional network or spectrum resources (apart from a minimal signaling overhead). Approximate communication is particularly useful to media delivery applications that can benefit significantly from unequal error protection of data bits. We show the usefulness of this method to such applications by designing and implementing an end-to-end media delivery system, called Apex. Our Software Defined Radio (SDR)-based experiments reveal that Apex can improve video quality by 5–20 dB [peak signal-to-noise ratio (PSNR)] across a diverse set of wireless conditions when compared to traditional approaches. We believe that mechanisms such as Apex can be a cornerstone in designing future wireless media delivery systems under any error-prone channel condition.