This paper introduces NeCTAr (Near-Cache Transformer Accelerator), a 16nm heterogeneous multicore RISC-V SoC for sparse and dense machine learning kernels with both near-core and near-memory accelerators. A prototype chip runs at 400MHz at 0.85V and performs matrix-vector multiplications with 109 GOPs/W. The effectiveness of the design is demonstrated by running inference on a sparse language model, ReLU-Llama.

Image classification for long-tailed scenarios has attracted more attention because its distribution is more similar to real-world image data. From the perspective of solving imbalance at the sample level, we propose a simple but effective method, named Sample-aware Knowledge Distillation (SAKD), which includes Selective Knowledge Distillation module and Stable Feature Center Learning module. The former conducts knowledge distillation at the sample-level by selecting samples, in which whether the sample needs to be distilled and to what extent is determined by evaluating the teacher network’s predictions for this sample. The latter is used to obtaining the stable feature center and making the feature center free from perturbation by hard samples, then further improving the classification boundary. We conduct extensive experiments on several long-tailed benchmark datasets and these results demonstrate that SAKD is effective. In addition, our SFCL module can be combined with other methods and also improve their performance.

Recent progress in programmable switches provides opportunities for service function chains (SFCs) provision to cloud tenants, which has the advantage of flexible deployment and high performance. We devise SFP for such SFC provision in the cloud. SFP's data plane installs physical NFs and is virtualized to host logical SFCs from multiple tenants. SFP's control plane uses a relaxed integer programming model to jointly optimize the placement of physical and logical NFs, which can achieve resource efficiency and high tenant traffic processing throughput within efficient execution time. Our prototype and evaluation shows that SFP can significantly offload NFV computation from server to the switch and maximize the switch resource utilization.

With the cloud networks being equipped with programmable switch, Service Function Chain (SFC) provision has started to be migrated to the switches for better performance and manageability. In this paper, we design HyperSFP which places multiple SFCs to a data center network (DCN). In the placement algorithm, HyperSFP builds an integer programming (IP) model to achieve functionality, fault tolerance, and load balance. To support large-scale networks, HyperSFP IP model is relaxed to two approximate approaches: Stage-Separated IP model and linear programming (LP) model. Both approaches can improve the algorithm efficiency. HyperSFP’s data plane is designed to deploy the active and backup NFs in the control-plane plan, and migrate traffic from failed active NFs to its backup NFs. Our prototype and evaluation shows that HyperSFP achieves performance gain by implementing NFs on programmable switches, its control plane achieves fault tolerance, load balance, and scalability, and its data plane can handle network failures promptly.

Remediation systems in modern information infrastructure gradually take over the increasing workload of unexpected events from operators. The core logic in such systems — remediation rule— still needs operators to fill in manually, which is a tedious and error-prone process. We propose Workflow Retrieval System (a.k.a. WRS), which helps the operator to build remediation rules.WRS is a recommendation system that recommends structures from existing rules to operators when they are creating new ones. WRS formalizes the workflows in remediation rules as trees and extracts representative atomic structures from them. Then WRS organizes atomic structures in two ways to accelerate the later retrieval — a two-level hierarchy which helps searching similar structures, and a keyword indexing structure which helps searching by words. With the two retrieval structures, we build two applications: one is real-time workflow auto completion application which recommends remaining structures based on the existing partial structure, and another is workflow recommendation where WRS recommends atomic structures based on the description of the remediation symptoms.We prototype WRS and evaluate it with legacy device vendors’ operation manual. Our evaluation shows that WRS reasonably extracts and organizes atomic structures in remediation workflows, and the two applications atop it show fast execution time and high accuracy.

NFV ecosystem is flourishing and more and more NF platforms appear, but this makes NF vendors difficult to deliver NFs rapidly to diverse platforms. We propose an NF development framework named NFD for cross-platform NF development. NFD's main idea is to decouple the functional logic from the platform logic -it provides a platform-independent language to program NFs' behavior models, and a compiler with interfaces to develop platform-specific plugins. By enabling a plugin on the compiler, various NF models would be compiled to executables integrated with the target platform. We prototype NFD, build 14 NFs, and support 6 platforms (standard Linux, OpenNetVM, GPU, SGX, DPDK, OpenNF). Our evaluation shows that NFD can save development workload for cross-platform NFs and output valid and performant NFs.

Mobile devices have been an integral part of our everyday lives. Users’ increasing interaction with mobile devices brings in significant concerns on various types of potential privacy leakage, among which location privacy draws the most attention. Specifically, mobile users’ trajectories constructed by location data may be captured by adversaries to infer sensitive information. In previous studies, differential privacy has been utilized to protect published trajectory data with rigorous privacy guarantee. Strong protection provided by differential privacy distorts the original locations or trajectories using stochastic noise to avoid privacy leakage. In this article, we propose a novel location inference attack framework, iTracker, which simultaneously recovers multiple trajectories from differentially private trajectory data using the structured sparsity model. Compared with the traditional recovery methods based on single trajectory prediction, iTracker, which takes advantage of the correlation among trajectories discovered by the structured sparsity model, is more effective in recovering multiple private trajectories simultaneously. iTracker successfully attacks the existing privacy protection mechanisms based on differential privacy. We theoretically demonstrate the near-linear runtime of iTracker, and the experimental results using two real-world datasets show that iTracker outperforms existing recovery algorithms in recovering multiple trajectories.

We present Variational Aspect-based Latent Topic Allocation (VALTA), a family of autoencoding topic models that learn aspect-based representations of reviews. VALTA defines a user-item encoder that maps bag-of-words vectors for combined reviews associated with each paired user and item onto structured embeddings, which in turn define per-aspect topic weights. We model individual reviews in a structured manner by inferring an aspect assignment for each sentence in a given review, where the per-aspect topic weights obtained by the user-item encoder serve to define a mixture over topics, conditioned on the aspect. The result is an autoencoding neural topic model for reviews, which can be trained in a fully unsupervised manner to learn topics that are structured into aspects. Experimental evaluation on large number of datasets demonstrates that aspects are interpretable, yield higher coherence scores than non-structured autoencoding topic model variants, and can be utilized to perform aspect-based comparison and genre discovery.

Ensuring high video quality of experience (QoE) on the broadcaster side is critical for interactive live streaming. However, measurements on multiple live streaming platforms show that they all suffer from broadcaster-side video quality degradation in the presence of transient bandwidth fluctuations. This paper presents Greedy Variable Bitrate (GVBR), a suite of solutions that optimizes the QoE through an approriate keyframe interval that trades cross-frame compression for lowered inter-frame interdependency, a simple-yet-efficient frame dropping strategy to prevent excessive frame drops, and a bitrate adaptation strategy customized for broadcasters who have shallow buffer. We compare GVBR with state-of-art algorithms in different network conditions, and find that GVBR can cut video interruption incidents by 90%, while achieving comparable bitrate.

Anomalous subgraph detection has been successfully applied to event detection in social media. However, the subgraph detection problembecomes challenging when the social media network incorporates abundant attributes, which leads to a multivariate network. The multivariate characteristic makes most existing methods incapable to tackle this problem effectively and efficiently, as it involves joint feature selection and subgraph detection that has not been well addressed in the current literature, especially, in the dynamic multivariate networks in which attributes evolve over time.