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- Genre: Academic theses
Description
Generating real-world content for VR is challenging in terms of capturing and processing at high resolution and high frame-rates. The content needs to represent a truly immersive experience, where the user can look around in 360-degree view and perceive the depth of the scene. The existing solutions only capture and offload the compute load to the server. But offloading large amounts of raw camera feeds takes longer latencies and poses difficulties for real-time applications. By capturing and computing on the edge, we can closely integrate the systems and optimize for low latency. However, moving the traditional stitching algorithms to battery constrained device needs at least three orders of magnitude reduction in power. We believe that close integration of capture and compute stages will lead to reduced overall system power.
We approach the problem by building a hardware prototype and characterize the end-to-end system bottlenecks of power and performance. The prototype has 6 IMX274 cameras and uses Nvidia Jetson TX2 development board for capture and computation. We found that capturing is bottlenecked by sensor power and data-rates across interfaces, whereas compute is limited by the total number of computations per frame. Our characterization shows that redundant capture and redundant computations lead to high power, huge memory footprint, and high latency. The existing systems lack hardware-software co-design aspects, leading to excessive data transfers across the interfaces and expensive computations within the individual subsystems. Finally, we propose mechanisms to optimize the system for low power and low latency. We emphasize the importance of co-design of different subsystems to reduce and reuse the data. For example, reusing the motion vectors of the ISP stage reduces the memory footprint of the stereo correspondence stage. Our estimates show that pipelining and parallelization on custom FPGA can achieve real time stitching.
We approach the problem by building a hardware prototype and characterize the end-to-end system bottlenecks of power and performance. The prototype has 6 IMX274 cameras and uses Nvidia Jetson TX2 development board for capture and computation. We found that capturing is bottlenecked by sensor power and data-rates across interfaces, whereas compute is limited by the total number of computations per frame. Our characterization shows that redundant capture and redundant computations lead to high power, huge memory footprint, and high latency. The existing systems lack hardware-software co-design aspects, leading to excessive data transfers across the interfaces and expensive computations within the individual subsystems. Finally, we propose mechanisms to optimize the system for low power and low latency. We emphasize the importance of co-design of different subsystems to reduce and reuse the data. For example, reusing the motion vectors of the ISP stage reduces the memory footprint of the stereo correspondence stage. Our estimates show that pipelining and parallelization on custom FPGA can achieve real time stitching.
ContributorsGunnam, Sridhar (Author) / LiKamWa, Robert (Thesis advisor) / Turaga, Pavan (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2018
Description
Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movements. Many researchers advocate pushing processing close to the sensor to substantially reduce data movements. However, continuous near-sensor processing raises the sensor temperature, impairing the fidelity of imaging/vision tasks.
The work characterizes the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. The characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, the characterization also identifies opportunities -- unique to the needs of near-sensor processing -- to regulate temperature based on dynamic visual task requirements and rapidly increase capture quality on demand.
Based on the characterization, the work proposes and investigate two thermal management strategies -- stop-capture-go and seasonal migration -- for imaging-aware thermal management. The work present parameters that govern the policy decisions and explore the trade-offs between system power and policy overhead. The work's evaluation shows that the novel dynamic thermal management strategies can unlock the energy-efficiency potential of near-sensor processing with minimal performance impact, without compromising image fidelity.
The work characterizes the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. The characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, the characterization also identifies opportunities -- unique to the needs of near-sensor processing -- to regulate temperature based on dynamic visual task requirements and rapidly increase capture quality on demand.
Based on the characterization, the work proposes and investigate two thermal management strategies -- stop-capture-go and seasonal migration -- for imaging-aware thermal management. The work present parameters that govern the policy decisions and explore the trade-offs between system power and policy overhead. The work's evaluation shows that the novel dynamic thermal management strategies can unlock the energy-efficiency potential of near-sensor processing with minimal performance impact, without compromising image fidelity.
ContributorsKodukula, Venkatesh (Author) / LiKamWa, Robert (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Brunhaver, John (Committee member) / Arizona State University (Publisher)
Created2019
Description
Mixed reality mobile platforms co-locate virtual objects with physical spaces, creating immersive user experiences. To create visual harmony between virtual and physical spaces, the virtual scene must be accurately illuminated with realistic physical lighting. To this end, a system was designed that Generates Light Estimation Across Mixed-reality (GLEAM) devices to continually sense realistic lighting of a physical scene in all directions. GLEAM optionally operate across multiple mobile mixed-reality devices to leverage collaborative multi-viewpoint sensing for improved estimation. The system implements policies that prioritize resolution, coverage, or update interval of the illumination estimation depending on the situational needs of the virtual scene and physical environment.
To evaluate the runtime performance and perceptual efficacy of the system, GLEAM was implemented on the Unity 3D Game Engine. The implementation was deployed on Android and iOS devices. On these implementations, GLEAM can prioritize dynamic estimation with update intervals as low as 15 ms or prioritize high spatial quality with update intervals of 200 ms. User studies across 99 participants and 26 scene comparisons reported a preference towards GLEAM over other lighting techniques in 66.67% of the presented augmented scenes and indifference in 12.57% of the scenes. A controlled lighting user study on 18 participants revealed a general preference for policies that strike a balance between resolution and update rate.
To evaluate the runtime performance and perceptual efficacy of the system, GLEAM was implemented on the Unity 3D Game Engine. The implementation was deployed on Android and iOS devices. On these implementations, GLEAM can prioritize dynamic estimation with update intervals as low as 15 ms or prioritize high spatial quality with update intervals of 200 ms. User studies across 99 participants and 26 scene comparisons reported a preference towards GLEAM over other lighting techniques in 66.67% of the presented augmented scenes and indifference in 12.57% of the scenes. A controlled lighting user study on 18 participants revealed a general preference for policies that strike a balance between resolution and update rate.
ContributorsPrakash, Siddhant (Author) / LiKamWa, Robert (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Hansford, Dianne (Committee member) / Arizona State University (Publisher)
Created2018
Description
In UAVs and parking lots, it is typical to first collect an enormous number of pixels using conventional imagers. This is followed by employment of expensive methods to compress by throwing away redundant data. Subsequently, the compressed data is transmitted to a ground station. The past decade has seen the emergence of novel imagers called spatial-multiplexing cameras, which offer compression at the sensing level itself by providing an arbitrary linear measurements of the scene instead of pixel-based sampling. In this dissertation, I discuss various approaches for effective information extraction from spatial-multiplexing measurements and present the trade-offs between reliability of the performance and computational/storage load of the system. In the first part, I present a reconstruction-free approach to high-level inference in computer vision, wherein I consider the specific case of activity analysis, and show that using correlation filters, one can perform effective action recognition and localization directly from a class of spatial-multiplexing cameras, called compressive cameras, even at very low measurement rates of 1\%. In the second part, I outline a deep learning based non-iterative and real-time algorithm to reconstruct images from compressively sensed (CS) measurements, which can outperform the traditional iterative CS reconstruction algorithms in terms of reconstruction quality and time complexity, especially at low measurement rates. To overcome the limitations of compressive cameras, which are operated with random measurements and not particularly tuned to any task, in the third part of the dissertation, I propose a method to design spatial-multiplexing measurements, which are tuned to facilitate the easy extraction of features that are useful in computer vision tasks like object tracking. The work presented in the dissertation provides sufficient evidence to high-level inference in computer vision at extremely low measurement rates, and hence allows us to think about the possibility of revamping the current day computer systems.
ContributorsKulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Thesis advisor) / Li, Baoxin (Committee member) / Chakrabarti, Chaitali (Committee member) / Sankaranarayanan, Aswin (Committee member) / LiKamWa, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
Nowadays, demand from the Internet of Things (IoT), automotive networking, and video applications is driving the transformation of Ethernet. It is a shift towards time-sensitive Ethernet. As a large amount of data is transmitted, many errors occur in the network. For this increased traffic, a Time Sensitive Network (TSN) is important. Time-Sensitive Network (TSN) is a technology that provides a definitive service for time sensitive traffic in an Ethernet environment that provides time-synchronization. In order to efficiently manage these errors, countermeasures against errors are required. A system that maintains its function even in the event of an internal fault or failure is called a Fault-Tolerant system. For this, after configuring the network environment using the OMNET++ program, machine learning was used to estimate the optimal alternative routing path in case an error occurred in transmission. By setting an alternate path before an error occurs, I propose a method to minimize delay and minimize data loss when an error occurs. Various methods were compared.
First, when no replication environment and secondly when ideal replication, thirdly random replication, and lastly replication using ML were tested. In these experiments, replication in an ideal environment showed the best results, which is because everything is optimal. However, except for such an
ideal environment, replication prediction using the suggested ML showed the best results.
These results suggest that the proposed method is effective, but there may be problems
with efficiency and error control, so an additional overview is provided for further
improvement.
ContributorsLee, Sang hee (Author) / Reisslein, Martin (Thesis advisor) / LiKamWa, Robert (Committee member) / Thyagaturu, Akhilesh (Committee member) / Arizona State University (Publisher)
Created2022
Description
Traditional sports coaching involves face-to-face instructions with athletes or playingback 2D videos of athletes’ training. However, if the coach is not in the same area
as the athlete, then the coach will not be able to see the athlete’s full body and thus
cannot give precise guidance to the athlete, limiting the athlete’s improvement. To
address these challenges, this paper proposes Augmented Coach, an augmented reality
platform where coaches can view, manipulate and comment on athletes’ movement
volumetric video data remotely via the network. In particular, this work includes a).
Capturing the athlete’s movement video data with Kinects and converting it into point
cloud format b). Transmitting the point cloud data to the coach’s Oculus headset via
5G or wireless network c). Coach’s commenting on the athlete’s joints. In addition,
the evaluation of Augmented Coach includes an assessment of its performance from
five metrics via the wireless network and 5G network environment, but also from the
coaches’ and athletes’ experience of using it. The result shows that Augmented Coach
enables coaches to instruct athletes from a distance and provide effective feedback for
correcting athletes’ motions under the network.
ContributorsQiao, Yunhan (Author) / LiKamWa, Robert (Thesis advisor) / Bansal, Ajay (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2023
Description
I present my work on a scalable and programmable I/O controller for region-based computing, which will be used in a rhythmic pixel-based camera pipeline. I provide a breakdown of the development and design of the I/O controller and how it fits in to rhythmic pixel regions, along with a studyon memory traffic of rhythmic pixel regions and how this translates to energy efficiency. This rhythmic pixel region-based camera pipeline has been jointly developed through Dr. Robert LiKamWa’s research lab. High spatiotemporal resolutions allow high precision for vision applications, such as for detecting features for augmented reality or face detection. High spatiotemporal resolution also comes with high memory throughput, leading to higher energy usage. This creates a tradeoff between high precision and energy efficiency, which becomes more important in mobile systems. In addition, not all pixels in a frame are necessary for the vision application, such as pixels that make up the background. Rhythmic pixel regions aim to reduce the tradeoff by creating a pipeline that allows an application developer to specify regions to capture at a non-uniform spatiotemporal resolution. This is accomplished by encoding the incoming image, and only sending the pixels within these specified regions. Later these encoded representations will be decoded to a standard frame representation usable by traditional vision applications. My contribution to this effort has been the design, testing and evaluation of the I/O controller.
ContributorsNguyen, Van (Author) / LiKamWa, Robert (Thesis advisor) / Jayasuriya, Suren (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
5G Millimeter Wave (mmWave) technology holds great promise for Connected Autonomous Vehicles (CAVs) due to its ability to achieve data rates in the Gbps range. However, mmWave suffers high beamforming overhead and requirement of line of sight (LOS) to maintain a strong connection. For Vehicle-to-Infrastructure (V2I) scenarios, where CAVs connect to roadside units (RSUs), these drawbacks become apparent. Because vehicles are dynamic, there is a large potential for link blockages, which in turn is detrimental to the connected applications running on the vehicle, such as cooperative perception and remote driver takeover. Existing RSU selection schemes base their decisions on signal strength and vehicle trajectory alone, which is not enough to prevent the blockage of links. Most recent CAVs motion planning algorithms routinely use other vehicle's near-future plans, either by explicit communication among vehicles, or by prediction. In this thesis, I make use of this knowledge (of the other vehicle's near future path plans) to further improve the RSU association mechanism for CAVs. I solve the RSU association problem by converting it to a shortest path problem with the objective to maximize the total communication bandwidth.
Evaluations of B-AWARE in simulation using Simulated Urban Mobility (SUMO) and Digital twin for self-dRiving Intelligent VEhicles (DRIVE) on 12 highway and city street scenarios with varying traffic density and RSU placements show that B-AWARE results in a 1.05x improvement of the potential datarate in the average case and 1.28x in the best case vs. the state of the art. But more impressively, B-AWARE reduces the time spent with no connection by 48% in the average case and 251% in the best case as compared to the state-of-the-art methods. This is partly a result of B-AWARE reducing almost 100% of blockage occurrences in simulation.
ContributorsSzeto, Matthew (Author) / Shrivastava, Aviral (Thesis advisor) / LiKamWa, Robert (Committee member) / Meuth, Ryan (Committee member) / Arizona State University (Publisher)
Created2023
Description
Efficient visual sensing plays a pivotal role in enabling high-precision applications in augmented reality and low-power Internet of Things (IoT) devices. This dissertation addresses the primary challenges that hinder energy efficiency in visual sensing: the bottleneck of pixel traffic across camera and memory interfaces and the energy-intensive analog readout process in image sensors. To overcome the bottleneck of pixel traffic, this dissertation proposes a visual sensing pipeline architecture that enables application developers to dynamically adapt the spatial resolution and update rates for specific regions within the scene. By selectively capturing and processing high-resolution frames only where necessary, the system significantly reduces energy consumption associated with memory traffic. This is achieved by encoding only the relevant pixels from the commercial image sensors with standard raster-scan pixel read-out patterns, thus minimizing the data stored in memory. The stored rhythmic pixel region stream is decoded into traditional frame-based representations, enabling seamless integration into existing video pipelines. Moreover, the system includes runtime support that allows flexible specification of the region labels, giving developers fine-grained control over the resolution adaptation process. Experimental evaluations conducted on a Xilinx Field Programmable Gate Array (FPGA) platform demonstrate substantial reductions of 43-64% in interface traffic, while maintaining controllable task accuracy. In addition to the pixel traffic bottleneck, the dissertation tackles the energy intensive analog readout process in image sensors. To address this, the dissertation proposes aggressive scaling of the analog voltage supplied to the camera. Extensive characterization on off-the-shelf sensors demonstrates that analog voltage scaling can significantly reduce sensor power, albeit at the expense of image quality. To mitigate this trade-off, this research develops a pipeline that allows application developers to adapt the sensor voltage on a frame-by-frame basis. A voltage controller is integrated into the existing Raspberry Pi (RPi) based video streaming pipeline, generating the sensor voltage. On top of that, the system provides a software interface for vision applications to specify the desired voltage levels. Evaluation of the system across a range of voltage scaling policies on popular vision tasks demonstrates that the technique can deliver up to 73% sensor power savings while maintaining reasonable task fidelity.
ContributorsKodukula, Venkatesh (Author) / LiKamWa, Robert (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Brunhaver, John (Committee member) / Nambi, Akshay (Committee member) / Arizona State University (Publisher)
Created2023
Description
Realistic lighting is important to improve immersion and make mixed reality applications seem more plausible. To properly blend the AR objects in the real scene, it is important to study the lighting of the environment. The existing illuminationframeworks proposed by Google’s ARCore (Google’s Augmented Reality Software Development Kit) and Apple’s ARKit (Apple’s Augmented Reality Software Development Kit) are computationally expensive and have very slow refresh rates, which make them incompatible for dynamic environments and low-end mobile devices. Recently, there have been other illumination estimation frameworks such as GLEAM, Xihe, which aim at providing better illumination with faster refresh rates. GLEAM is an illumination estimation framework that understands the real scene by collecting pixel data from a reflecting spherical light probe. GLEAM uses this data to form environment cubemaps which are later mapped onto a reflection probe to generate illumination for AR objects.
It is noticed that from a single viewpoint only one half of the light probe can be observed at a time which does not give complete information about the environment. This leads to the idea of having a multi-viewpoint estimation for better performance. This thesis work analyzes the multi-viewpoint capabilities of AR illumination frameworks that use physical light probes to understand the environment. The current work builds networking using TCP and UDP protocols on GLEAM. This thesis work also documents how processor load sharing has been done while networking devices and how that benefits the performance of GLEAM on mobile devices. Some enhancements using multi-threading have also been made to the already existing GLEAM model to improve its performance.
ContributorsGurram, Sahithi (Author) / LiKamWa, Robert (Thesis advisor) / Jayasuriya, Suren (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2022