We introduce a Power-of-Two post-training quantization( PTQ) method for deep neural network that meets hardware requirements and does not call for long-time retraining. PTQ requires a small set of calibration data and is easier for deployment, but results in lower accuracy than Quantization-Aware Training( QAT). Power-of-Two quantization can convert the multiplication introduced by quantization and dequantization to bit-shift that is adopted by many efficient accelerators. However, the Power-of-Two scale has fewer candidate values, which leads to more rounding or clipping errors. We propose a novel Power-of-Two PTQ framework, dubbed RAPQ, which dynamically adjusts the Power-of-Two scales of the whole network instead of statically determining them layer by layer. It can theoretically trade off the rounding error and clipping error of the whole network. Meanwhile, the reconstruction method in RAPQ is based on the BN information of every unit. Extensive experiments on ImageNet prove the excellent performance of our proposed method. Without bells and whistles, RAPQ can reach accuracy of 65% and 48% on ResNet-18 and MobileNetV2 respectively with weight INT2 activation INT4. We are the first to propose PTQ for the more constrained but hardware-friendly Power-of-Two quantization and prove that it can achieve nearly the same accuracy as SOTA PTQ method. The code will be released.

We present SAMPLES: Self Adaptive Mining of Persistent LExical Snippets; a systematic framework for classifying network traffic generated by mobile applications. SAMPLES constructs conjunctive rules, in an automated fashion, through a supervised methodology over a set of labeled flows (the training set).

Mobile Internet availability, performance and reliability have remained stubbornly opaque since the rise of cellular data access. Conducting network measurements can give us insight into user-perceived network conditions, but doing so requires careful consideration of device state and efficient use of scarce resources. Existing approaches address these concerns in ad-hoc ways.

No abstract available.

While the availability and accessibility of cellular network connectivity have improved in recent years, our ability to diagnose and debug network problems in this environment has not. One key challenge is that many of the network problems occur near the edge of the network where only mobile devices can perceive them, but network and battery resources to conduct measurements from these mobile devices are scarce. Traditional network measurement approaches that use continuous, periodic, or random measurements are either infeasible or ineffective in this environment.

We consider a simple multiple access network in which a destination node receives information from multiple sources via a set of relay nodes. Each relay node has access to a subset of the sources, and is connected to the destination by a unit capacity link. Arbitrary errors may be introduced by up to z of the relay nodes. We propose an efficient distributed error correction coding scheme, where the relay nodes encode independently such that the overall codewords received at the destination are codewords from a single Reed-Solomon code. We show that it achieves the full capacity region for up to three sources.

Mobilyzer is an open-source network measurement library that coordinates network measurement tasks among different applications, facilitates measurement task design, and allows for more effective measurement task management than in existing standalone approaches. Unifying various network tasks into one framework greatly simplifies the problem of developing, deploying and managing measurement tasks which may otherwise interfere with one another. An intelligent scheduler, coordinated by a central server, dynamically schedules tasks to run in the background, preserving the user's battery life and respecting limits set by the user on task frequency and data consumption. We will demo MobiPerf, an open-source mobile network measurement tool built using the Mobilyzer library. MobiPerf collects a wide range of network performance data, ranging from the latency and throughput measurements common in existing client-based measurement frameworks, to HTTP loading times for specific URLs, to inferring RRC state configuration parameters and their impact on performance. We will also demo an interface for viewing a large, open dataset of performance data from around the world collected by MobiPerf.

We consider the problem of communicating information over a network secretly and reliably in the presence of a hidden adversary who can eavesdrop and inject malicious errors. We provide polynomial-time distributed network codes that are information-theoretically rate-optimal for this scenario, improving on the rates achievable in prior work by Ngai Our main contribution shows that as long as the sum of the number of links the adversary can jam (denoted by ZO) and the number of links he can eavesdrop on (denoted by ZI) is less than the network capacity (denoted by C) (i.e., ), our codes can communicate (with vanishingly small error probability) a single bit correctly and without leaking any information to the adversary. We then use this scheme as a module to design codes that allow communication at the source rate of C- ZO when there are no security requirements, and codes that allow communication at the source rate of C- ZO- ZI while keeping the communicated message provably secret from the adversary. Interior nodes are oblivious to the presence of adversaries and perform random linear network coding; only the source and destination need to be tweaked. We also prove that the rate-region obtained is information-theoretically optimal. In proving our results, we correct an error in prior work by a subset of the authors in this paper.

This paper studies the overhead for channel gain monitoring in wireless networks with time division multiple access. We first investigate the scenario in which a receiver needs to track the channel gains with respect to multiple transmitters. Suppose that there are n transmitters, and no more than k channels suffer significant variations since the last round. We prove that “Θ(k log(n=k)) time slots” is the minimum overhead needed to catch up with the k varied channels. We propose a novel channel-gain monitoring scheme named ADMOT. ADMOT leverages recent advances in compressive sensing in signal processing and interference processing in wireless communication, to enable the receiver to estimate all n channels in a reliable and computationally efficient manner within O(k log(n=k)) time slots. To our best knowledge, all previous channel-tracking schemes require Θ(n) time slots regardless of k.

This paper studies rateless network error correction codes for reliable multicast in the presence of adversarial errors. We present rateless coding schemes for two adversarial models, where the source sends more redundancy over time, until decoding succeeds. The first model assumes there is a secret channel between the source and the destination that the adversaries cannot overhear. The rate of the channel is negligible compared to the main network. In the second model the source and destination share random secrets independent of the input information. The amount of secret information required is negligible compared to the amount of information sent. Both schemes are capacity optimal, distributed, polynomial-time and end-to-end in that other than the source and destination nodes, other intermediate nodes carry out classical random linear network coding.

Multi-user multiple-input multiple-output (MU-MIMO) is the latest communication technology that promises to linearly increase the wireless capacity by deploying more antennas on access points (APs). However, the large number of MIMO antennas will generate a huge amount of digital signal samples in real time. This imposes a grand challenge on the AP design by multiplying the computation and the I/O requirements to process the digital samples. This paper presents BigStation, a scalable architecture that enables realtime signal processing in large-scale MIMO systems which may have tens or hundreds of antennas. Our strategy to scale is to extensively parallelize the MU-MIMO processing on many simple and low-cost commodity computing devices. Our design can incrementally support more antennas by proportionally adding more computing devices. To reduce the overall processing latency, which is a critical constraint for wireless communication, we parallelize the MU-MIMO processing with a distributed pipeline based on its computation and communication patterns. At each stage of the pipeline, we further use data partitioning and computation partitioning to increase the processing speed. As a proof of concept, we have built a BigStation prototype based on commodity PC servers and standard Ethernet switches. Our prototype employs 15 PC servers and can support real-time processing of 12 software radio antennas. Our results show that the BigStation architecture is able to scale to tens to hundreds of antennas. With 12 antennas, our BigStation prototype can increase wireless capacity by 6.8x with a low mean processing delay of 860μs. While this latency is not yet low enough for the 802.11 MAC, it already satisfies the real-time requirements of many existing wireless standards, e.g., LTE and WCDMA.

Passive network tomography uses end-to-end observations of network communications to characterize the network, for instance, to estimate the network topology and to localize random or adversarial faults. Under the setting of linear network coding, this work provides a comprehensive study of passive network tomography in the presence of network (random or adversarial) faults. To be concrete, this work is developed along two directions: 1) tomographic upper and lower bounds (i.e., the most adverse conditions in each problem setting under which network tomography is possible, and corresponding schemes (computationally efficient, if possible) that achieve this performance) are presented for random linear network coding (RLNC). We consider RLNC designed with common randomness, i.e., the receiver knows the random codebooks of all intermediate nodes. (To justify this, we show an upper bound for the problem of topology estimation in networks using RLNC without common randomness.) In this setting, we present the first set of algorithms that characterize the network topology exactly. Our algorithm for topology estimation with random network errors has time complexity that is polynomial in network parameters. For the problem of network error localization given the topology information, we present the first computationally tractable algorithm to localize random errors, and prove that it is computationally intractable to localize adversarial errors. 2) New network coding schemes are designed that improve the tomographic performance of RLNC while maintaining the desirable low-complexity, throughput-optimal, distributed linear network coding properties of RLNC. In particular, we design network codes based on Reed–Solomon codes so that a maximal number of adversarial errors can be localized in a computationally efficient manner even without the information of network topology. The tomography schemes proposed in the paper can be used to monitor networks with other faults such as packet losses and link delays, etc.

The paper studies the construction of error-estimation-codes (EEC), which estimate the bit-error-rate (BER) of packet transmissions. The concept of EEC was first proposed by Chen et.al in SIGCOMM 2010. In the same work group-sampling-error-estimation-codes (GSEEC) were constructed. Assuming the packet length is n, GSEEC requires communication overhead O(log(n)) and coding complexity O(n) to reliably estimate BER. In this paper, random walk based error-estimation-codes (RAKEE) are proposed, which are able to achieve constant communication overhead O(1) and linear coding complexity O(n). In the end of the paper, numerical experiments is shown to support our theoretical analysis.

Network coding provides a promising alternative to traditional store-and-forward transmission paradigm. However, due to its information-mixing nature, network coding is notoriously susceptible to pollution attacks: a single polluted packet can end up corrupting bunches of good ones. Existing authentication mechanisms either incur high computation/bandwidth overheads, or cannot resist the tag pollution proposed recently. This paper presents a novel idea termed “padding for orthogonality” for network coding authentication. Inspired by it, we design a public-key based signature scheme and a symmetric-key based MAC scheme, which can both effectively contain pollution attacks at forwarders. In particular, we combine them to propose a unified scheme termed MacSig, the first hybrid-key cryptographic approach to network coding authentication. It can thwart both normal pollution and tag pollution attacks in an efficient way. Simulative results show that our MacSig scheme has a low bandwidth overhead, and a verification process 2–4 times faster than typical signature-based solutions in some circumstances.

An equivalence result is established between the Shannon capacity and the stable capacity of communication networks. Given a discrete-time network with memoryless, time-invariant, discrete-output channels, it is proved that the Shannon capacity equals the stable capacity. The results treat general demands (e.g., multiple unicast demands) and apply even when neither the Shannon capacity nor the stable capacity is known for the given demands. The result also generalize from discrete-alphabet channels to Gaussian channels.

This work considers the multiple-access multicast error-correction scenario over a packetized network withzmalicious edge adversaries. The network has min-cutmand packets of lengthl, and each sink demands all information from the set of sourcesS. The capacity region is characterized for both a “side-channel” model (where sources and sinks share some random bits that are secret from the adversary) and an “omniscient” adversarial model (where no limitations on the adversary's knowledge are assumed). In the “side-channel” adversarial model, the use of a secret channel allows higher rates to be achieved compared to the “omniscient” adversarial model, and a polynomial-complexity capacity-achieving code is provided. For the “omniscient” adversarial model, two capacity-achieving constructions are given: the first is based on random subspace code design and has complexity exponential inlm, while the second uses a novel multiple-field-extension technique and has O(lm|S|) complexity, which is polynomial in the network size. Our code constructions are “end-to-end” in that all nodes except the sources and sinks are oblivious to the adversaries and may simply implement predesigned linear network codes (random or otherwise). Also, the sources act independently without knowledge of the data from other sources.

By allowing routers to randomly mix the information content in packets before forwarding them, network coding can maximize network throughput in a distributed manner with low complexity. However, such mixing also renders the transmission vulnerable to pollution attacks, where a malicious node injects corrupted packets into the information flow. In a worst case scenario, a single corrupted packet can end up corrupting all the information reaching a destination. In this paper, we propose RIPPLE, a symmetric key based in-network scheme for network coding authentication. RIPPLE allows a node to efficiently detect corrupted packets and encode only the authenticated ones. Despite using symmetric key based homomorphic Message Authentication Code (MAC) algorithms, RIPPLE achieves asymmetry by delayed disclosure of the MAC keys. Our work is the first symmetric key based solution to allow arbitrary collusion among adversaries. It is also the first to consider tag pollution attacks, where a single corrupted MAC tag can cause numerous packets to fail authentication farther down the stream, effectively emulating a successful pollution attack.

Network Tomography (or network monitoring) uses end-to-end measurements to characterize the network, such as estimating the network topology and localizing random or adversarial glitches. Under the setting that all nodes in the network perform random linear network coding, this work provides a comprehensive study of passive network tomography in the presence of network failures, in particular adversarial/random errors and adversarial/random erasures. Our results are categorized into two classes: 1. Topology Estimation. In the presence of both adversarial/random failures, we prove it is both necessary and sufficient for all nodes in the network to share common randomness, i.e., the receiver knows the random code-books of other nodes. Without such common randomness, we prove that in the presence of adversarial or random failures it is either theoretically impossible or computationally intractable to estimate topology accurately. With common randomness, we present the first set of algorithms for characterizing topology exactly. Our algorithms for topology estimation in the presence of random errors/erasures have polynomial-time complexity. 2. Failure Localization. Given the topology, we present the first polynomial time algorithms to localize random errors and adversarial erasures. For the problem of locating adversarial errors, we prove that it is intractable.

The network communication scenario where one or more receivers request all the information transmitted by different sources is considered. We introduce the first polynomial-time (in network size) network codes that achieve any point inside the rate-region for the problem of multiple-source multicast in the presence of malicious errors, for any fixed number of sources. Our codes are fully distributed and different sources require no knowledge of the data transmitted by their peers. Our codes are “end-to-end”, that is, all nodes apart from the sources and the receivers are oblivious to the adversaries present in the network and simply implement random linear network coding.