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1. SyncWISE: Window Induced Shift Estimation for Synchronization of Video and Accelerometry from Wearable Sensors

   https://doi.org/10.1145/3411824  

   Zhang, Yun C.; Zhang, Shibo; Liu, Miao; Daly, Elyse; Battalio, Samuel; Kumar, Santosh; Spring, Bonnie; Rehg, James M.; Alshurafa, Nabil (September 2020, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   null (Ed.)

   The development and validation of computational models to detect daily human behaviors (e.g., eating, smoking, brushing) using wearable devices requires labeled data collected from the natural field environment, with tight time synchronization of the micro-behaviors (e.g., start/end times of hand-to-mouth gestures during a smoking puff or an eating gesture) and the associated labels. Video data is increasingly being used for such label collection. Unfortunately, wearable devices and video cameras with independent (and drifting) clocks make tight time synchronization challenging. To address this issue, we present the Window Induced Shift Estimation method for Synchronization (SyncWISE) approach. We demonstrate the feasibility and effectiveness of our method by synchronizing the timestamps of a wearable camera and wearable accelerometer from 163 videos representing 45.2 hours of data from 21 participants enrolled in a real-world smoking cessation study. Our approach shows significant improvement over the state-of-the-art, even in the presence of high data loss, achieving 90% synchronization accuracy given a synchronization tolerance of 700 milliseconds. Our method also achieves state-of-the-art synchronization performance on the CMU-MMAC dataset. 

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2. Perceptual Compression for Video Storage and Processing Systems

   https://doi.org/10.1145/3357223.3362725  

   Mazumdar, Amrita; Haynes, Brandon; Balazinska, Magda; Ceze, Luis; Cheung, Alvin; Oskin, Mark (November 2019, Proceedings of the ACM Symposium on Cloud Computing)

   null (Ed.)

   Compressed videos constitute 70% of Internet traffic, and video upload growth rates far outpace compute and storage improvement trends. Past work in leveraging perceptual cues like saliency, i.e., regions where viewers focus their perceptual attention, reduces compressed video size while maintaining perceptual quality, but requires significant changes to video codecs and ignores the data management of this perceptual information. In this paper, we propose Vignette, a compression technique and storage manager for perception-based video compression in the cloud. Vignette complements off-the-shelf compression software and hardware codec implementations. Vignette's compression technique uses a neural network to predict saliency information used during transcoding, and its storage manager integrates perceptual information into the video storage system. Our results demonstrate the benefit of embedding information about the human visual system into the architecture of cloud video storage systems. 

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3. CURE: A High-Performance, Low-Power, and Reliable Network-on-Chip Design Using Reinforcement Learning

   https://doi.org/10.1109/TPDS.2020.2986297  

   Wang, Ke; Louri, Ahmed (April 2020, IEEE Transactions on Parallel and Distributed Systems)

   We propose CURE, a deep reinforcement learning (DRL)-based NoC design framework that simultaneously reduces network latency, improves energy-efficiency, and tolerates transient errors and permanent faults. CURE has several architectural innovations and a DRL-based hardware controller to manage design complexity and optimize trade-offs. First, in CURE, we propose reversible multi-function adaptive channels (RMCs) to reduce NoC power consumption and network latency. Second, we implement a new fault-secure adaptive error correction hardware in each router to enhance reliability for both transient errors and permanent faults. Third, we propose a router power-gating and bypass design that powers off NoC components to reduce power and extend chip lifespan. Further, for the complex dynamic interactions of these techniques, we propose using DRL to train a proactive control policy to provide improved fault-tolerance, reduced power consumption, and improved performance. Simulation using the PARSEC benchmark shows that CURE reduces end-to-end packet latency by 39%, improves energy efficiency by 92%, and lowers static and dynamic power consumption by 24% and 38%, respectively, over conventional solutions. Using mean-time-to-failure, we show that CURE is 7.7x more reliable than the conventional NoC design. 

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4. Dynamic Heterogeneous Voltage Regulation for Systolic Array-Based DNN Accelerators

   https://doi.org/10.1109/ICCD50377.2020.00088  

   Chen, Jianhao; Riad, Joseph; Sanchez-Sinencio, Edgar; Li, Peng (October 2020, 2020 IEEE 38th International Conference on Computer Design (ICCD))

   null (Ed.)

   With the growing performance and wide application of deep neural networks (DNNs), recent years have seen enormous efforts on DNN accelerator hardware design for platforms from mobile devices to data centers. The systolic array has been a popular architectural choice for many proposed DNN accelerators with hundreds to thousands of processing elements (PEs) for parallel computing. Systolic array-based DNN accelerators for datacenter applications have high power consumption and nonuniform workload distribution, which makes power delivery network (PDN) design challenging. Server-class multicore processors have benefited from distributed on-chip voltage regulation and heterogeneous voltage regulation (HVR) for improving energy efficiency while guaranteeing power delivery integrity. This paper presents the first work on HVR-based PDN architecture and control for systolic array-based DNN accelerators. We propose to employ a PDN architecture comprising heterogeneous on-chip and off-chip voltage regulators and multiple power domains. By analyzing patterns of typical DNN workloads via a modeling framework, we propose a DNN workload-aware dynamic PDN control policy to maximize system energy efficiency while ensuring power integrity. We demonstrate significant energy efficiency improvements brought by the proposed PDN architecture, dynamic control, and power gating, which lead to a more than five-fold reduction of leakage energy and PDN energy overhead for systolic array DNN accelerators. 

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5. Efficient Video Redaction at the Edge: Human Motion Tracking for Privacy Protection

   https://doi.org/10.1145/3762994  

   Qiao, Haotian; Srinivas, Vidya; Dinda, Peter; Dick, Robert (August 2025, ACM Transactions on Embedded Computing Systems)

   Computationally efficient, camera-based, real-time human position tracking on low-end, edge devices would enable numerous applications, including privacy-preserving video redaction and analysis. Unfortunately, running most deep neural network based models in real time requires expensive hardware, making widespread deployment difficult, particularly on edge devices. Shifting inference to the cloud increases the attack surface, generally requiring that users trust cloud servers, and increases demands on wireless networks in deployment venues. Our goal is to determine the extreme to which edge video redaction efficiency can be taken, with a particular interest in enabling, for the first time, low-cost, real-time deployments with inexpensive commodity hardware. We present an efficient solution to the human detection (and redaction) problem based on singular value decomposition (SVD) background removal and describe a novel time- and energy-efficient sensor-fusion algorithm that leverages human position information in real-world coordinates to enable real-time visual human detection and tracking at the edge. These ideas are evaluated using a prototype built from (resource-constrained) commodity hardware representative of commonly used low-cost IoT edge devices. The speed and accuracy of the system are evaluated via a deployment study, and it is compared with the most advanced relevant alternatives. The multi-modal system operates at a frame rate ranging from 20 FPS to 60 FPS, achieves awIoU_0.3score (see Section 5.4) ranging from 0.71 to 0.79, and successfully performs complete redaction of privacy-sensitive pixels with a success rate of 91%–99% in human head regions and 77%–91% in upper body regions, depending on the number of individuals present in the field of view. These results demonstrate that it is possible to achieve adequate efficiency to enable real-time redaction on inexpensive, commodity edge hardware. 

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