DwarvesGraph: A High-Performance Graph Mining System with Pattern Decomposition

This paper presents DwarvesGraph, the first graph mining system that decomposes the target pattern into several subpatterns, and then computes the count of each. The results of the target pattern can be calculated using the subpattern counts with very low additional cost. Despite decomposition-based algorithms have been studied for years, we propose several novel techniques to address key system challenges: 1) a partial-embedding-centric programming model with efficient supports for pattern existence query and advanced graph mining applications such as FSM; 2) an accurate and efficient cost model based on approximate graph mining; 3) an efficient search method to jointly determine the decomposition of all concrete patterns of an application, considering the computation cost and cross-pattern computation reuse; and 4) the partial symmetry breaking technique to eliminate redundant enumeration for each subpattern while preserving equivalence of computation. Our experiments show that DwarvesGraph is significantly faster than all existing state-of-the-art systems and provides a novel and viable path to scale to large patterns

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.Ministerio de Economía y Competitividad TEC2016-77785-

Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.Comment: Now also consider 0/1 binary activations. Memory access statistics reporte

MIDAS: Mutual Information Driven Approximate Synthesis

Applications ranging from the Internet of Things (IoT) to high-performance computing demand energy-efficient hardware for processing and storage. Reducing computation accuracy has shown the potential to achieve high energy efficiency in hardware implementations. In recent years, several automatic approximate logic synthesis techniques have been proposed to build an approximate circuit systematically, trading off accuracy for hardware cost. In this paper, we propose a novel approximate logic synthesis technique to simplify circuits using mutual information by considering the input distribution. Our experimental result shows that our proposed methodology demonstrates improvements in terms of area, delay, and error compared to the state-of-the-art