More Like this

1. Multi-queue fair queueing

   Mohammad Hedayati, Kai Shen ( July 2019 , Proceedings of the 2019 Usenix Annual Technical Conference)

   Modern high-speed devices (e.g., network adapters, storage, accelerators) use new host interfaces, which expose multiple software queues directly to the device. These multi-queue interfaces allow mutually distrusting applications to access the device without any cross-core interaction, enabling throughput in the order of millions of IOP/s on multicore systems. Unfortunately, while independent device access is scalable, it also introduces a new problem: unfairness. Mechanisms that were used to provide fairness for older devices are no longer tenable in the wake of multi-queue design, and straightforward attempts to re-introduce it would require cross-core synchronization that undermines the scalability for which multiple queues were designed. To address these challenges, we present Multi-Queue Fair Queueing (MQFQ), the first fair, work-conserving scheduler suitable for multi-queue systems. Specifically, we (1) reformulate a classical fair queueing algorithm to accommodate multiqueue designs, and (2) describe a scalable implementation that bounds potential unfairness while minimizing synchronization overhead. Our implementation of MQFQ in Linux 4.15 demonstrates both fairness and high throughput. Evaluation with an NVMe over RDMA fabric (NVMf) device shows that MQFQ can reach up to 3.1 Million IOP/s on a single machine--20× higher than the state-of-the-art Linux Budget Fair Queueing. Compared to a system with no fairness, MQFQmore »reduces the slowdown caused by an antagonist from 3:78× to 1:33× for the FlashX workload and from 6:57× to 1:03× for the Aerospike workload (2× is considered "fair" slowdown).« less
2. FABRIC: A National-ScaleProgrammable ExperimentalNetwork Infrastructure

   https://doi.org/0.1109/MIC.2019.2958545  

   Baldin, Ilya ; Nikolich, Anita ; Griffioen, James ; Monga, Indermohan ; Wang, Kuang-Ching ; Lehman, Tom ; Ruth, Paul ( January 2020 , IEEE internet computing)

   FABRIC is a unique national research infrastructure to enable cutting-edge andexploratory research at-scale in networking, cybersecurity, distributed computing andstorage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elementsequipped with large amounts of compute and storage, interconnected by high speed,dedicated optical links. It will connect a number of specialized testbeds for cloudresearch (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5Gtechnologies (Platforms for Advanced Wireless Research or PAWR), as well as productionhigh-performance computing facilities and science instruments to create a rich fabric fora wide variety of experimental activities.
3. Efficient Defect Identification via Oxide Memristive Crossbar Array Based Morphological Image Processing

   https://doi.org/10.1002/aisy.202000202  

   Lee, Hee Sung ; Baek, Yongmin ; Lin, Qiubao ; Minsu Chen, Joseph ; Park, Minseong ; Lee, Doeon ; Kim, Sihwan ; Lee, Kyusang ( November 2020 , Advanced Intelligent Systems)

   Wang, Huan (Ed.)

   Defect identification has been a significant task in various fields to prevent the potential problems caused by imperfection. There is great attention for developing technology to accurately extract defect information from the image using a computing system without human error. However, image analysis using conventional computing technology based on Von Neumann structure is facing bottlenecks to efficiently process the huge volume of input data at low power and high speed. Herein efficient defect identification is demonstrated via a morphological image process with minimal power consumption using an oxide transistor and a memristor‐based crossbar array that can be applied to neuromorphic computing. Using a hardware and software codesigned neuromorphic system combined with a dynamic Gaussian blur kernel operation, an enhanced defect detection performance is successfully demonstrated with about 104 times more power‐efficient computation compared to the conventional complementary metal‐oxide semiconductor (CMOS)‐based digital implementation. It is believed the back end of line (BEOL)‐compatible all‐oxide‐based memristive crossbar array provides the unique potential toward universal artificial intelligence of things (AIoT) applications where conventional hardware can hardly be used.
4. Is neuron coverage a meaningful measure for testing deep neural networks?

   https://doi.org/10.1145/3368089.3409754  

   Harel-Canada, Fabrice ; Wang, Lingxiao ; Gulzar, Muhammad Ali ; Gu, Quanquan ; Kim, Miryung ( October 2020 , the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020))

   Recent effort to test deep learning systems has produced an intuitive and compelling test criterion called neuron coverage (NC), which resembles the notion of traditional code coverage. NC measures the proportion of neurons activated in a neural network and it is implicitly assumed that increasing NC improves the quality of a test suite. In an attempt to automatically generate a test suite that increases NC, we design a novel diversity promoting regularizer that can be plugged into existing adversarial attack algorithms. We then assess whether such attempts to increase NC could generate a test suite that (1) detects adversarial attacks successfully, (2) produces natural inputs, and (3) is unbiased to particular class predictions. Contrary to expectation, our extensive evaluation finds that increasing NC actually makes it harder to generate an effective test suite: higher neuron coverage leads to fewer defects detected, less natural inputs, and more biased prediction preferences. Our results invoke skepticism that increasing neuron coverage may not be a meaningful objective for generating tests for deep neural networks and call for a new test generation technique that considers defect detection, naturalness, and output impartiality in tandem.
5. SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

   Wu, Bichen ; Iandola, Forrest ; Jin, Peter H ; Keutzer, Kurt ( December 2016 , arXiv.org)

   Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires realtime inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment. In this work, we propose SqueezeDet, a fully convolutional neural network for object detection that aims to simultaneously satisfy all of the above constraints. In our network we use convolutional layers not only to extract feature maps, but also as the output layer to compute bounding boxes and class probabilities. The detection pipeline of our model only contains a single forward pass of a neural network, thus it is extremely fast. Our model is fully convolutional, which leads to small model size and better energy efficiency. Finally, our experiments show that our model is very accurate, achieving state-of-the-art accuracy on the KITTI [9] benchmark. The source code of SqueezeDet is open-source released1.