Cache compressions are proven effective in improving the performance of caches in conventional processors. They compress data into a smaller size, allowing caches to accommodate more blocks. This helps reduce cache misses and expensive memory accesses, ultimately improving performance. However, conventional cache compression is less effective for energy harvesting systems (EHSs) which experience frequent power failure, as many compressed blocks end up not being used before their loss upon power outage. This wastes hard-won energy, which would otherwise be used for making more program progress. To address this issue, this paper introduces Kagura, an adaptive cache compression extension with frequent power failure in mind. Specifically, Kagura disables cache compression when it finds out that many cached blocks are unlikely to be reused before the next power outage. That way, Kagura avoids the energy waste on useless compressions/decompressions, and the resulting speedup is on par with the ideal intermittenceaware cache compressor. Experimental results show that when combined with an existing cache compressor, Kagura reduces the total energy consumption by an average of$\mathbf{4. 5 3 \%}$(up to 16.21 %) and improves the performance by an average of 4.74 % (up to$\mathbf{1 7. 8 7 \%}$) compared to the baseline EHS without cache compression.

Existing dead block predictors have proven to be effective in reducing cache leakage power of conventional systems. However, prior work is significantly less effective in energy harvesting systems in that it does not take into account their unique characteristic, i.e., frequent power failure during program execution. Even if some cache blocks are predicted to be live, they may not be used due to their loss upon power failure. In response, this paper introduces EDBP, an extension to existing dead block predictors, to enhance their performance in various energy harvesting environments. EDBP can identify and deactivate those cache blocks that are not reused before upcoming power failure-though they are considered live by the existing predictor-thereby lowering cache leakage and preserving more energy for forward execution progress. Experimental results show that for 20 applications from Mediabench and MiBench, EDBP alone reduces total energy consumption by 6.5% and improves performance by 6.9% compared to the baseline with no dead block predictor. When combined with a conventional dead block predictor (Cache Decay), EDBP achieves 9.8% reduction in total energy consumption—approaching the theoretical minimum—and 11.9% performance improvement.

Prefetching improves performance by reducing cache misses.However, conventional prefetchers are too aggressive to serve batteryless energy harvesting systems (EHSs) where energy efficiency is the utmost design priority due to weak input energy and the resulting frequent outages.To this end, this paper proposes IPEX, an Intermittence-aware Prefetching EXtension that can be integrated into existing prefetchers on EHSs.IPEX aims to avert useless prefetches by suppressing the prefetching of the cache blocks receiving no hit before their loss on power failure, which would otherwise waste harvested energy.At a proper moment before an upcoming outage, IPEX throttles the prefetch degree to target only those blocks that are likely to be used before the outage.That way IPEX saves energy and spends it on making further execution progress.Experimental results show that on average, IPEX reduces energy consumption by 7.86% (up to 21.64%) and improves performance by 8.96% (up to 23.49%) compared to a conventional prefetcher.

Energy harvesting systems rely on either rollback or roll-forward recovery to resume power-interrupted program correctly. However, both recovery schemes have their own inherent drawbacks. To this end, this paper presents RollSwitch, a hybrid power failure recovery scheme that can achieve low-cost yet high-performance intermittent computation for energy harvesting systems. According to the underlying energy harvesting condition, RollSwitch dynamically switches between rollback and roll-forward recovery modes to maximize the performance. In particular, RollSwitch leverages the level of available energy in the capacitor as a proxy for determining the appropriate recovery mode. For this purpose, RollSwitch devises a simple capacitor energy predictor whose outcome governs the recovery mode selection in the near future. The experimental results demonstrate that RollSwitch achieves 15.0% and 19.8% performance gain on average over the state-of-art rollback and roll-forward recovery schemes, respectively.

Android apps having access to private information may be legitimate, depending on whether the app provides users enough semantics to justify the access. Existing works analyzing app semantics are coarse-grained, staying on the app-level. They can only identify whether an app, as a whole, should request special permission but cannot answer whether a specific app behavior under a particular runtime context, such as information flow, is correctly justified. We proposeFlowCog, an automated system to extract semantics related to information flows and correlate such semantics with given information flows to address these issues. Particularly,FlowCogstatically finds all the Android views related to the given flow via control or data dependencies and then extracts semantics, such as texts and images, from these views and associated layouts. Next, FlowCog adopts natural language processing and deep learning approaches to infer whether the extracted semantics correlate with the given flow.FlowCogis open-source and available athttps://github.com/xcdu/FlowCog. Our evaluation shows thatFlowCogcan achieve an accuracy rate of 95.4% and an$\mathrm{F}_{1}$score of 0.953.

With the increasing adoption of mobile 4G LTE networks, video streaming as the major contributor of 4G LTE data traffic, has become extremely hot. However, the battery life has become the bottleneck when mobile users are using online video services. In this paper, we deploy a real mobile system for power measurement and profiling of online video streaming in 4G LTE networks. Based on some designed experiments with different configurations, we measure the power consumption for online video streaming, offline video playing, and mobile background. A RRC state study is taken to understand how RRC states impact power consumption. Then, we profile the power consumption of video streaming and show the results with different impact factors. According to our experimental statistics, the power saving room for online video streaming in 4G LTE networks can be up to 69%.

We present and demonstrate INFRAME++, a novel system that enables concurrent, dual-mode, full-frame communication for both users and devices. It achieves unobtrusive screen-camera data communication without affecting the primary video-viewing experience for human users. It leverages the capability discrepancy and distinctive features of the human vision system and devices (modern display and camera). We have implemented INFRAME++ as a PC-phone application. Both communication will be realized through multiplexed videos frames which will be displayed on a modern monitor (120FPS) and captured by a smartphone camera for data decoding. In this demonstration (Figure 1), we will show that INFRAME++ can yield normal video-viewing experience for humans, and high-rate data communication for devices (up to 300 kbps). User participation will be welcome in this live demo.

Recent efforts in visible light communication over screen-camera links have exploited the display for data communication. Such practices, albeit convenient, have led to contention between space allocated for users and content reserved for devices, in addition to their visual anti-aesthetics and distractedness. In this paper, we propose INFRAME++, a system that enables concurrent, dual-mode, full-frame communication for both users and devices. INFRAME++ leverages the spatial-temporal flicker-fusion property of human vision system and the fast frame rate of modern display. It multiplexes data onto full-frame video contents through novel complementary frame composition, hierarchical frame structure, and CDMA-like modulation. It thus ensures opportunistic and unobtrusive screen-camera data communication without affecting the primary video-viewing experience for human users. Our prototype and experiments have confirmed its effectiveness of delivering data to devices in its visual communication with imperceptible video artifacts for viewers. INFRAME++ is able to achieve 150-240 kbps at 120FPS over a 24? LCD monitor with one data frame per 12 display frames. It supports up to 360kbps while data:video is 1:6.