Exploiting GPU Architectures for Dynamic Invariant Mining

Dynamic mining of invariants is a class of approaches to extract logic formulas from the execution traces of a system under verification (SUV), with the purpose of expressing stable conditions in the behaviour of the SUV. The mined formulas represent likely invariants for the SUV, which certainly hold on the considered traces, but there is no guarantee that they are true in general. A large set of representative execution traces must be analysed to increase the probability that mined invariants are generally true. However, this becomes extremely time-consuming for current sequential approaches when long execution traces and large set of SUV variables are considered. To overcome this limitation, the paper presents a parallel approach for invariant mining that exploits GPU architectures for processing an execution trace composed of millions of clock cycles in few seconds

Mangrove: an Inference-based Dynamic Invariant Mining for GPU Architectures

Likely invariants model properties that hold in operating conditions of a computing system. Dynamic mining of invariants aims at extracting logic formulas representing such properties from the system execution traces, and it is widely used for verification of intellectual property (IP) blocks. Although the extracted formulas represent likely invariants that hold in the considered traces, there is no guarantee that they are true in general for the system under verification. As a consequence, to increase the probability that the mined invariants are true in general, dynamic mining has to be performed to large sets of representative execution traces. This makes the execution-based mining process of actual IP blocks very time-consuming due to the trace lengths and to the large sets of monitored signals. This article presents extit{Mangrove}, an efficient implementation of a dynamic invariant mining algorithm for GPU architectures. Mangrove exploits inference rules, which are applied at run time to filter invariants from the execution traces and, thus, to sensibly reduce the problem complexity. Mangrove allows users to define invariant templates and, from these templates, it automatically generates kernels for parallel and efficient mining on GPU architectures. The article presents the tool, the analysis of its performance, and its comparison with the best sequential and parallel implementations at the state of the art

System-level functional and extra-functional characterization of SoCs through assertion mining

Virtual prototyping is today an essential technology for modeling, verification, and re-design of full HW/SW platforms. This allows a fast prototyping of platforms with a higher and higher complexity, which precludes traditional verification approaches based on the static analysis of the source code. Consequently, several technologies based on the analysis of simulation traces have proposed to efficiently validate the entire system from both the functional and extra-functional point of view. From the functional point of view, different approaches based on invariant and assertion mining have been proposed in literature to validate the functionality of a system under verification (SUV). Dynamic mining of invariants is a class of approaches to extract logic formulas with the purpose of expressing stable conditions in the behavior of the SUV. The mined formulas represent likely invariants for the SUV, which certainly hold on the considered traces. A large set of representative execution traces must be analyzed to increase the probability that mined invariants are generally true. However, this is extremely time-consuming for current sequential approaches when long execution traces and large set of SUV's variables are considered. Dynamic mining of assertions is instead a class of approaches to extract temporal logic formulas with the purpose of expressing temporal relations among the variables of a SUV. However, in most cases, existing tools can only mine assertions compliant with a limited set of pre-defined templates. Furthermore, they tend to generate a huge amount of assertions, while they still lack an effective way to measure their coverage in terms of design behaviors. Moreover, the security vulnerability of a firmware running on a HW/SW platforms is becoming ever more critical in the functional verification of a SUV. Current approaches in literature focus only on raising an error as soon as an assertion monitoring the SUV fails. No approach was proposed to investigate the issue that this set of assertions could be incomplete and that different, unusual behaviors could remain not investigated. From the extra-functional point of view of a SUV, several approaches based on power state machines (PSMs) have been proposed for modeling and simulating the power consumption of an IP at system-level. However, while they focus on the use of PSMs as the underlying formalism for implementing dynamic power management techniques of a SoC, they generally do not deal with the basic problem of how to generate a PSM. In this context, the thesis aims at exploiting dynamic assertion mining to improve the current approaches for the characterization of functional and extra-functional properties of a SoC with the final goal of providing an efficient and effective system-level virtual prototyping environment. In detail, the presented methodologies focus on: efficient extraction of invariants from execution traces by exploiting GP-GPU architectures; extraction of human-readable temporal assertions by combining user-defined assertion templates, data mining and coverage analysis; generation of assertions pinpointing the unlike execution paths of a firmware to guide the analysis of the security vulnerabilities of a SoC; and last but not least, automatic generation of PSMs for the extra-functional characterization of the SoC

Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness

In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small perturbations to an input do not cause the network to perform misclassifications. In this paper, we present a novel algorithm for verifying robustness properties of neural networks. Our method synergistically combines gradient-based optimization methods for counterexample search with abstraction-based proof search to obtain a sound and ({\delta}-)complete decision procedure. Our method also employs a data-driven approach to learn a verification policy that guides abstract interpretation during proof search. We have implemented the proposed approach in a tool called Charon and experimentally evaluated it on hundreds of benchmarks. Our experiments show that the proposed approach significantly outperforms three state-of-the-art tools, namely AI^2 , Reluplex, and Reluval

Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining

Dual encoder models are ubiquitous in modern classification and retrieval. Crucial for training such dual encoders is an accurate estimation of gradients from the partition function of the softmax over the large output space; this requires finding negative targets that contribute most significantly ("hard negatives"). Since dual encoder model parameters change during training, the use of traditional static nearest neighbor indexes can be sub-optimal. These static indexes (1) periodically require expensive re-building of the index, which in turn requires (2) expensive re-encoding of all targets using updated model parameters. This paper addresses both of these challenges. First, we introduce an algorithm that uses a tree structure to approximate the softmax with provable bounds and that dynamically maintains the tree. Second, we approximate the effect of a gradient update on target encodings with an efficient Nystrom low-rank approximation. In our empirical study on datasets with over twenty million targets, our approach cuts error by half in relation to oracle brute-force negative mining. Furthermore, our method surpasses prior state-of-the-art while using 150x less accelerator memory.Comment: To appear at AISTATS 202

A branch & bound algorithm to determine optimal bivariate splits for oblique decision tree induction

Univariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only

Community Detection using Locality Statistics

The goal of community detection is to identify clusters and groups of vertices that share common properties or play similar roles in a graph, using only the information encoded in the graph. Our work analyzes two methods of identifying an anomalous community in temporal graphs and another method of identifying active communities in a static massive graph. All methods are based on locality statistics. In [50], an anomalous community is detected that shows growing connectivities in a time series of graphs. We formulate the task as a hypothesis-testing problem in stochastic block model time series. We derive the limiting properties and power characteristics of two competing test statistics built on distinct underlying locality statistics. In addition, we provide applicable implementations of two competing test statistics and detailed experimental results for a neural imaging application in [36]. In [51], active communities are detected in a static massive graph on which many community detection algorithms scale poorly. We propose a novel framework for detecting active communities that consist of the most active vertices. Our framework utilizes a parallelizable trimming algorithm based on a locality statistic to filter out inactive vertices, and then clusters the remaining active vertices via spectral decomposition of their similarity matrix. The framework is applicable to graphs consisting of billions of vertices and hundreds of billions of edges. In summary, this work provides developments in community detection, in both temporal graphs and static massive graphs, by employing locality statistics

Accurate classification of RNA structures using topological fingerprints

While RNAs are well known to possess complex structures, functionally similar RNAs often have little sequence similarity. While the exact size and spacing of base-paired regions vary, functionally similar RNAs have pronounced similarity in the arrangement, or topology, of base-paired stems. Furthermore, predicted RNA structures often lack pseudoknots (a crucial aspect of biological activity), and are only partially correct, or incomplete. A topological approach addresses all of these difficulties. In this work we describe each RNA structure as a graph that can be converted to a topological spectrum (RNA fingerprint). The set of subgraphs in an RNA structure, its RNA fingerprint, can be compared with the fingerprints of other RNA structures to identify and correctly classify functionally related RNAs. Topologically similar RNAs can be identified even when a large fraction, up to 30%, of the stems are omitted, indicating that highly accurate structures are not necessary. We investigate the performance of the RNA fingerprint approach on a set of eight highly curated RNA families, with diverse sizes and functions, containing pseudoknots, and with little sequence similarity–an especially difficult test set. In spite of the difficult test set, the RNA fingerprint approach is very successful (ROC AUC \u3e 0.95). Due to the inclusion of pseudoknots, the RNA fingerprint approach both covers a wider range of possible structures than methods based only on secondary structure, and its tolerance for incomplete structures suggests that it can be applied even to predicted structures. Source code is freely available at https://github.rcac.purdue.edu/mgribsko/XIOS_RNA_fingerprint