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This paper presents a multi-channel memory based architecture for parallel processing of large-scale graph traversal for fieldprogrammable gate array (FPGA). By designing a multi-channel memory subsystem with two DRAM modules and two SRAM chips and developing an optimized pipelining structure for the processing elements, we achieve superior performance to that of a state-of-the-art highly optimized BFS implementations using the same type of FPGA.

This paper presents the design and implementation of a high performance sparse matrix-vector multiplication (SpMV) on fieldprogrammable gate array (FPGA). By proposing a new storage format to compress the indexes of non-zero elements by exploiting the substructure of the sparse matrix, our SpMV implementation on a reconfigurable computing platform with a multi-channel memory subsystem is capable of obtaining similar performance by using a single FPGA to that of a highly optimized BFS implementation on a commercial heterogeneous system containing four FPGAs.

Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). Our work in this paper improves the computing scalability of lightweight applications and breaks the memory bottleneck of using deep learning method on memory-limited devices.

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