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Spiking Neural Networks (SNNs) have gained considerable attention due to the energy-efficient and multiplication-free characteristics. The continuous growth in scale of deep SNNs poses challenges for model deployment. Network pruning reduces hardware resource requirements of model deployment by compressing the network scale. However, existing SNN pruning methods cause high pruning costs and performance loss because the pruning iterations amplify the training difficulty of SNNs. In this paper, inspired by the critical brain hypothesis in neuroscience, we propose a regeneration mechanism based on the neuron criticality for SNN pruning to enhance feature extraction and accelerate the pruning process. Firstly, we propose a low-cost metric for the criticality in SNNs. Then, we re-rank the pruned structures after pruning and regenerate those with higher criticality to obtain the critical network. Our method achieves higher performance than the current state-of-the-art (SOTA) method with up to 95.26% reduction of pruning cost. Moreover, we investigate the underlying mechanism of our method and find that it efficiently selects potential structures and learns the consistent feature representation.

Subgraph matching is a NP-complete problem that extracts isomorphic embeddings of a query graph $q$ in a data graph $G$. In this paper, we present a framework with three components: Preprocessing, Reordering and Enumeration. While pruning is the core technique for almost all existing subgraph matching solvers, it mainly eliminates unnecessary enumeration over data graph without alternation of query graph. By formulating a problem: Assignment under Conditional Candidate Set(ACCS), which is proven to be equivalent to Subgraph matching problem, we propose Dynamic Graph Editing(DGE) that is for the first time designed to tailor the query graph to achieve pruning effect and performance acceleration. As a result, we proposed DGEE(Dynamic Graph Editing Enumeration), a novel enumeration algorithm combines Dynamic Graph Editing and Failing Set optimization. Our second contribution is proposing fGQL , an optimized version of GQL algorithm, that is utilized during the Preprocessing phase. Extensive experimental results show that the DGEE-based framework can outperform state-of-the-art subgraph matching algorithms.

In the big data era, graph computing is widely used to exploit the hidden value in real-world graphs in various scenarios such as social networks, knowledge graphs, web searching, and recommendation systems. However, the random memory accesses result in inefficient use of cache and the irregular degree distribution leads to substantial load imbalance. Breadth-First Search (BFS) is frequently utilized as a kernel for many important and complex graph algorithms. In this paper, we describe a preprocessing approach using Reverse Cuthill-Mckee (RCM) algorithm to improve data locality and demonstrate how to achieve an efficient load balancing for BFS. Computations on RCM-reordered graph data are also accelerated with SIMD executions. We evaluate the performance of the graph preprocessing approach on Kronecker graphs of the Graph500 benchmark and real-world graphs. Our BFS implementation on RCM-reordered graph data achieves 326.48 MTEPS/W (mega TEPS per watt) on an ARMv8 system, ranking 2nd on the Green Graph500 list in June 2020 (the 1st rank uses GPU acceleration).

The subgraph-centric programming model is a promising approach and has been applied in many state-of-the-art distributed graph computing frameworks. However, traditional graph partition algorithms have significant difficulties in processing large-scale power-law graphs. The major problem is the communication bottleneck found in many subgraph-centric frameworks. Detailed analysis indicates that the communication bottleneck is caused by the huge communication volume or the extreme message imbalance among partitioned subgraphs. The traditional partition algorithms do not consider both factors at the same time, especially on power-law graphs. In this paper, we propose a novel efficient and balanced vertex-cut graph partition algorithm (EBV) which grants appropriate weights to the overall communication cost and communication balance. We observe that the number of replicated vertices and the balance of edge and vertex assignment have a great influence on communication patterns of distributed subgraph-centric frameworks, which further affect the overall performance. Based on this insight, We design an evaluation function that quantifies the proportion of replicated vertices and the balance of edges and vertices assignments as important parameters. Besides, we sort the order of edge processing by the sum of end-vertices' degrees from small to large. Experiments show that EBV reduces replication factor and communication by at least 21.8% and 23.7% respectively than other self-based partition algorithms. When deployed in the subgraph-centric framework, it reduces the running time on power-law graphs by an average of 16.8% compared with the state-of-the-art partition algorithm. Our results indicate that EBV has a great potential in improving the performance of subgraph-centric frameworks for the parallel large-scale power-law graph processing.

The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications. In situations where rigorous guarantees regarding the optimality of solutions are not required, MVWCP is usually solved using stochastic local search (SLS) algorithms, which also define the state of the art for solving this problem. However, there is no single SLS algorithm which gives the best performance across all classes of MVWCP instances, and it is challenging to effectively identify the most suitable algorithm for each class of MVWCP instances. In this work, we follow the paradigm of Programming by Optimization (PbO) to develop a new, flexible and highly parametric SLS framework for solving MVWCP, combining, for the first time, a broad range of effective heuristic mechanisms. By automatically configuring this PbO-MWC framework, we achieve substantial advances in the state-of-the-art in solving MVWCP over a broad range of prominent benchmarks, including two derived from real-world applications in transplantation medicine (kidney exchange) and assessment of research excellence.

The Fast Fourier Transform(FFT) is a classic signal processing algorithm that is utilized in a wide range of applications. For image processing, FFT computes on every pixel's value of an image, regardless of their properties in frequency domain. The Sparse Fast Fourier Transform (SFFT) is an innovative algorithm for discrete Fourier transforms on signals that possess characteristics of the sparsity in frequency domain. A reference implementation of the algorithm has been proven to be efficient than modern FFT library in cases of sufficient sparsity. However, the SFFT implementation has a critical drawback that it only works reliably for very specific input parameters, especially signal sparsity $k$, which hinders the extensive application of SFFT. In this paper, we propose an Adaptive Tuning Sparse Fast Fourier Transform (ATSFFT), which is a novel sparse fast fourier transform enabled with sparsity detection. In the case of unknown sparsity $k$, ATSFFT is capable of probing the sparsity $k$ via adaptive dynamic tuning and completing the sparse Fourier transform. Experimental results show that ATSFFT outperforms SFFT while it is able to control the computation error better than SFFT. Furthermore, ATSFFT achieves an order of magnitude of performance improvement than the state-of-the-art FFT library, FFTW.

Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the objective. Most of existing approaches model WSOD as a multiple instance learning (MIL) problem. However, we observe that the result of MIL based detector is unstable, i.e., the most confident bounding boxes change significantly when using different initializations. We quantitatively demonstrate the instability by introducing a metric to measure it, and empirically analyze the reason of instability. Although the instability seems harmful for detection task, we argue that it can be utilized to improve the performance by fusing the results of differently initialized detectors. To implement this idea, we propose an end-to-end framework with multiple detection branches, and introduce a simple fusion strategy. We further propose an orthogonal initialization method to increase the difference between detection branches. By utilizing the instability, we achieve 52.6% and 48.0% mAP on the challenging PASCAL VOC 2007 and 2012 datasets, which are both the new state-of-the-arts., Comment: Accepted by CVPR 2019 Workshop

Nowadays, in the big data era, social networks, graph databases, knowledge graphs, electronic commerce etc. demand efficient and scalable capability to process an ever increasing volume of graph-structured data. To meet the challenge, two mainstream distributed programming models, vertex-centric (VC) and subgraph-centric (SC) were proposed. Compared to the VC model, the SC model converges faster with less communication overhead on well-partitioned graphs, and is easy to program due to the "think like a graph" philosophy. The edge-cut method is considered as a natural choice of subgraph-centric model for graph partitioning, and has been adopted by Giraph++, Blogel and GRAPE. However, the edge-cut method causes significant performance bottleneck for processing large scale power-law graphs. Thus, the SC model is less competitive in practice. In this paper, we present an innovative distributed graph computing framework, DRONE (Distributed gRaph cOmputiNg Engine). It combines the subgraph-centric model and the vertex-cut graph partitioning strategy. Experiments show that DRONE outperforms the state-of-art distributed graph computing engines on real-world graphs and synthetic power-law graphs. DRONE is capable of scaling up to process one-trillion-edge synthetic power-law graphs, which is orders of magnitude larger than previously reported by existing SC-based frameworks., Comment: 13 pages, 9 figures