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1. VisualWorldDB: A DBMS for the Visual World. Brandon Haynes, Maureen Daum, Amrita Mazumdar, Magdalena Balazinska, Alvin Cheung, and Luis Ceze. CIDR, 2020.

   Brandon Haynes, Maureen Daum (January 2020, Conference on Innovative Data Systems Research (CIDR))

   null (Ed.)

   Many recent video applications |including autonomous driving, traffic monitoring, drone analytics, large-scale surveillance networks, and virtual reality require reasoning about, combining, and operating over many video streams, each with distinct position and orientation. However, modern video data management systems are largely designed to process individual streams of video data as if they were independent and unrelated. In this paper, we present VisualWorldDB, a vision and an initial architecture for a new type of database management system optimized for multi-video applications. VisualWorldDB ingests video data from many perspectives and makes them queryable as a single multidimensional visual object. It incorporates new techniques for optimizing, executing, and storing multi-perspective video data. Our preliminary results suggest that this approach allows for faster queries and lower storage costs, improving the state of the art for applications that operate over this type of video data. 

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2. Profiling-free Configuration Adaptation and Latency-Aware Resource Scheduling for Video Analytics

   https://doi.org/10.1109/BigData55660.2022.10020573  

   Zhou, Tian; Wu, Fubao; Gao, Lixin (December 2022, 2022 IEEE International Conference on Big Data (Big Data))

   With increasingly deployed cameras and the rapid advances of Computer Vision, large-scale live video analytics becomes feasible. However, analyzing videos is compute-intensive. In addition, live video analytics needs to be performed in real time. In this paper, we design an edge server system for live video analytics. We propose to perform configuration adaptation without profiling video online. We select configurations with a prediction model based on object movement features. In addition, we reduce the latency through resource orchestration on video analytics servers. The key idea of resource orchestration is to batch inference tasks that use the same CNN model, and schedule tasks based on a priority value that estimates their impact on the total latency. We evaluate our system with two video analytic applications, road traffic monitoring and pose detection. The experimental results show that our profiling-free adaptation reduces the workload by 80% of the state-of-the-art adaptation without lowering the accuracy. The average serving latency is reduced by up to 95% comparing with the profiling-based adaptation. 

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3. QuantHD: A Quantization Framework for Hyperdimensional Computing

   https://doi.org/10.1109/TCAD.2019.2954472  

   Imani, M; Bosch, S; Datta, S; Ramakrishna, S; Salamat, S; Rabaey, J; Rosing, T. (November 2019, IEEE transactions on computeraided design of integrated circuits and systems)

   Brain-inspired Hyperdimensional (HD) computing models cognition by exploiting properties of high dimensional statistics– high-dimensional vectors, instead of working with numeric values used in contemporary processors. A fundamental weakness of existing HD computing algorithms is that they require to use floating point models in order to provide acceptable accuracy on realistic classification problems. However, working with floating point values significantly increases the HD computation cost. To address this issue, we proposed QuantHD, a novel framework for quantization of HD computing model during training. QuantHD enables HD computing to work with a low-cost quantized model (binary or ternary model) while providing a similar accuracy as the floating point model. We accordingly propose an FPGA implementation which accelerates HD computing in both training and inference phases. We evaluate QuantHD accuracy and efficiency on various real-world applications, and observe that QuantHD can achieve on average 17.2% accuracy improvement as compared to the existing binarized HD computing algorithms which provide a similar computation cost. In terms of efficiency, QuantHD FPGA implementation can achieve on average 42.3× and 4.7× (34.1× and 4.1×) energy efficiency improvement and speedup during inference (training) as compared to the state-of-the-art HD computing algorithms. 

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4. Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks

   https://doi.org/10.1109/ASP-DAC52403.2022.9712574  

   Goel, Abhinav; Tung, Caleb; Hu, Xiao; Thiruvathukal, George K.; Davis, James C.; Lu, Yung-Hsiang (January 2022, 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC))

   Computer vision on low-power edge devices enables applications including search-and-rescue and security. State-of-the-art computer vision algorithms, such as Deep Neural Networks (DNNs), are too large for inference on low-power edge devices. To improve efficiency, some existing approaches parallelize DNN inference across multiple edge devices. How-ever, these techniques introduce significant communication and synchronization overheads or are unable to balance workloads across devices. This paper demonstrates that the hierarchical DNN architecture is well suited for parallel processing on multiple edge devices. We design a novel method that creates a parallel inference pipeline for computer vision problems that use hierarchical DNNs. The method balances loads across the collaborating devices and reduces communication costs to facilitate the processing of multiple video frames simultaneously with higher throughput. Our experiments consider a representative computer vision problem where image recognition is performed on each video frame, running on multiple Raspberry Pi 4Bs. With four collaborating low-power edge devices, our approach achieves 3.21× higher throughput, 68% less energy consumption per device per frame, and a 58% decrease in memory when compared with existing sinaledevice hierarchical DNNs. 

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5. UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers

   https://doi.org/10.1109/PerCom59722.2024.10494467  

   Jia, Hong; Kwon, Young D; Mat, Dong; Pham, Nhat; Qendro, Lorena; Vu, Tam; Mascolo, Cecilia (March 2024, IEEE)

   Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers (MCUs). This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection. In this paper, we present UR2M, a novel Uncertainty and Resource-aware event detection framework for MCUs. Specifically, we (i) develop an uncertainty-aware WED based on evidential theory for accurate event detection and reliable uncertainty estimation; (ii) introduce a cascade ML framework to achieve efficient model inference via early exits, by sharing shallower model layers among different event models; (iii) optimize the deployment of the model and MCU library for system efficiency. We conducted extensive experiments and compared UR2M to traditional uncertainty baselines using three wearable datasets. Our results demonstrate that UR2M achieves up to 864% faster inference speed, 857% energy-saving for uncertainty estimation, 55% memory saving on two popular MCUs, and a 22% improvement in uncertainty quantification performance. UR2M can be deployed on a wide range of MCUs, significantly expanding real-time and reliable WED applications. 

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