Zero-knowledge proof (ZKP) is a cryptographic protocol that allows one party to prove the correctness of a statement to another party without revealing any information beyond the correctness of the statement itself. It guarantees computation integrity and confidentiality, and is therefore increasingly adopted in industry for a variety of privacy-preserving applications, such as verifiable outsource computing and digital currency.

Modern deep learning systems embrace the compilation idea to self generate code of a deep learning model to catch up the rapidly changed deep learning operators and newly emerged hardware platforms. The performance of the self-generated code is guaranteed via auto-tuning frameworks which normally take a long time to find proper execution schedules for the given operators, which hurts both user experiences and time-to-the-market in terms of model developments and deployments.

Video cameras have been deployed at scale today. Driven by the breakthrough in deep learning (DL), organizations that have deployed these cameras start to use DL-based techniques for live video analytics. Although existing systems aim to optimize live video analytics from a variety of perspectives, they are agnostic to the workload dynamics in real-world deployments. In this work, we present Distream, a distributed live video analytics system based on the smart camera-edge cluster architecture, that is able to adapt to the workload dynamics to achieve low-latency, high-throughput, and scalable live video analytics. The key behind the design of Distream is to adaptively balance the workloads across smart cameras and partition the workloads between cameras and the edge cluster. In doing so, Distream is able to fully utilize the compute resources at both ends to achieve optimized system performance. We evaluated Distream with 500 hours of distributed video streams from two real-world video datasets with a testbed that consists of 24 cameras and a 4-GPU edge cluster. Our results show that Distream consistently outperforms the status quo in terms of throughput, latency, and latency service level objective (SLO) miss rate.

We address the problem of serving Deep Neural Networks (DNNs) efficiently from a cluster of GPUs. In order to realize the promise of very low-cost processing made by accelerators such as GPUs, it is essential to run them at sustained high utilization. Doing so requires cluster-scale resource management that performs detailed scheduling of GPUs, reasoning about groups of DNN invocations that need to be co-scheduled, and moving from the conventional whole-DNN execution model to executing fragments of DNNs. Nexus is a fully implemented system that includes these innovations. In large-scale case studies on 16 GPUs, when required to stay within latency constraints at least 99% of the time, Nexus can process requests at rates 1.8-12.7X higher than state of the art systems can. A long-running multi-application deployment stays within 84% of optimal utilization and, on a 100-GPU cluster, violates latency SLOs on 0.27% of requests.

Recent advances have enabled oracle classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much simpler models. We formulate the problem of detecting the short-term skews online and exploiting models based on it as a new sequential decision making problem dubbed the Online Bandit Problem, and present a new algorithm to solve it. When applied to recognizing faces in TV shows and movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on GPU/CPU) relative to a state-of-the-art convolutional neural network, at competitive accuracy.

We consider applying computer vision to video on cloud-backed mobile devices using Deep Neural Networks (DNNs). The computational demands of DNNs are high enough that, without careful resource management, such applications strain device battery, wireless data, and cloud cost budgets. We pose the corresponding resource management problem, which we call Approximate Model Scheduling, as one of serving a stream of heterogeneous (i.e., solving multiple classification problems) requests under resource constraints. We present the design and implementation of an optimizing compiler and runtime scheduler to address this problem. Going beyond traditional resource allocators, we allow each request to be served approximately, by systematically trading off DNN classification accuracy for resource use, and remotely, by reasoning about on-device/cloud execution trade-offs. To inform the resource allocator, we characterize how several common DNNs, when subjected to state-of-the art optimizations, trade off accuracy for resource use such as memory, computation, and energy. The heterogeneous streaming setting is a novel one for DNN execution, and we introduce two new and powerful DNN optimizations that exploit it. Using the challenging continuous mobile vision domain as a case study, we show that our techniques yield significant reductions in resource usage and perform effectively over a broad range of operating conditions.

Always-on continuous sensing apps drain the battery quickly because they prevent the main processor from sleeping. Instead, sensor hub hardware, available in many smartphones today, can run continuous sensing at lower power while keeping the main processor idle. However, developers have to divide functionality between the main processor and the sensor hub. We implement MobileHub, a system that automatically rewrites applications to leverage the sensor hub without additional programming effort. MobileHub uses a combination of dynamic taint tracking and machine learning to learn when it is safe to leverage the sensor hub without affecting application semantics. We implement MobileHub in Android and prototype a sensor hub on a 8-bit AVR micro-controller. We experiment with 20 applications from Google Play. Our evaluation shows that MobileHub significantly reduces power consumption for continuous sensing apps.

Retransmissions reduce the efficiency of data communication in wireless networks because of: (i) per-retransmission packet headers, (ii) contention overhead on every retransmission, and (iii) redundant bits in every retransmission. In fact, every retransmission nearly doubles the time to successfully deliver the packet. To improve spectrum efficiency in a lossy environment, we propose a new in-frame retransmission scheme using uACKs. Instead of waiting for the entire transmission to end before sending the ACK, the receiver sends smaller uACKs for every few symbols, on a separate narrow feedback channel. Based on these uACKs, the sender only retransmits the lost symbols after the last data symbol in the frame, thereby adaptively changing the frame size to ensure it is successfully delivered. We have implemented uACK on the Sora platform. Experiments with our prototype validate the feasibility of symbol-level uACK . By significantly reducing the retransmistion overhead, the sender is able to aggressively use higher data rate for a lossy link. Both improve the overall network efficiency. Our experimental results from a controlled environment and an 9-node software radio testbed show that uACK can have up to 140% throughput gain over 802.11g and up to 60% gain over the best known retransmission scheme.

This demonstration shows a novel virtualization architecture, called Multi-Purpose Access Point (MPAP), which can virtualize multiple heterogenous wireless standards based on software radio. The basic idea is to deploy a wide-band radio front-end to receive wireless signals from all wireless standards sharing the same spectrum band, and use separate software base-bands to demodulate information stream for each wireless standard. Based on software radio, MPAP consolidates multiple wireless devices into single hardware platform, and allows them to share the same general-purpose computing resource. Different software base-bands can easily communicate and coordinate with one another. Thus, it also provides better coexistence among heterogenous wireless standards. As an example, we demonstrate to use non-contiguous OFDM in 802.11g PHY to avoid the mutual interference with narrow-band ZigBee communication.