[PDF] Top 20 Sparse matrix-vector multiplication on network-on-chip
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Sparse matrix-vector multiplication on network-on-chip
... system matrix A which is usually large and sparse (Elkurdi et ...largest sparse matrix ...target matrix is ex- tremely sparse, unsymmetrical and ... See full document
6
A Study on Network-On-Chip architecture using Genetic Algorithm
... specific Network-on-chip (NoC) topology and routes the communication traces on the interconnection ...network. Network-on-chip (NoC) is a new paradigm for designing scalable ... See full document
12
On-Chip Permutation Mesh Network for MPSOCs Network-on-Chip
... One of the proposed method is to extend the 2D- Network-on-Chip to the third dimension. In the past years, 3D-ICs have been attracted an attention as the potential solution to resolve the interconnect ... See full document
5
Journey From Optical Neural Networks To Photonic Chips
... The above optical implementations of different neural network architectures have become possible with the use of special purpose optical devices. For the real-time ONN implementation, the number of neurons and the ... See full document
5
Parallel Multicore CSB Format and Its Sparse Matrix Vector Multiplication
... There are some methods to improve the efficiency of SpMV. Autotuning used in pOSKI [2] and other solvers can optimize for different supercomputers and different problems. Cache partition [3] and reordering [4] can re- ... See full document
8
Streaming reduction circuit for sparse matrix vector multiplication in FPGAs
... Floating point sparse matrix vector multiplications (SM×V) are kernel operations for many scientific algorithms. In these algorithms, the SM×V is often responsible for the biggest part of the ... See full document
65
Sparse matrix vector multiplication on a field programmable gate array
... In the original scheme where the SMVM was only executed with only one vector, there was only one memory to hold the plan. This was possible because the new plan could directly overwrite the old plan with the same ... See full document
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FPGA Hardware Accelerators - Case Study on Design Methodologies and Trade-Offs
... on sparse matrix multiplication imple- mented on ...block-based multiplication in order to perform large matrix-matrix multiplication ... See full document
65
Accelerating Sparse Matrix Operations in Neural Networks on Graphics Processing Units
... manipulate sparse structures (Saad, 1990). By sparse, we mean operations that only require a small fraction of the elements in a tensor to output the correct ...for sparse scenarios in deep learning: ... See full document
10
Optimizing Sparse Matrix Vector Multiplications on an ARMv8 based Many Core Architecture
... Hardware Platforms. As depicted in Figure 1, FTP integrates 64 ARMv8 based Xiaomi cores. It offers a peak performance of 512 Gflops for double- precision operations, with a maximum power consumption of 100 Watts. The ... See full document
16
Floating point sparse matrix vector multiply for FPGAs
... We investigate SMVM on the VirtexII-6000-4. On a single microprocessor, SMVM performs somewhat worse than DMVM due to data structure interpretation overhead. In our FPGA imple- mentation (Chapter 2), data structure ... See full document
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A methodology for speeding up matrix vector multiplication for single/multi core architectures
... To our knowledge, there are only a few research works in optimizing the dense MVM software: [22], [23], [27], [43]. [22], [23] and [43] are MVM implementations on GPU architectures while [27] describes a parallel MVM ... See full document
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Sparse matrix computations for dynamic network centrality
... the network Enron and θ = 10 −4 for the network ...resulting matrix Q [M] for the dataset Enron is 5847 when (6b) or (6c) is used, and 1676 for ...centrality vector is obtained because we are ... See full document
19
Adaptive Optimization of Sparse Matrix Vector Multiplication on Emerging Many Core Architectures
... representing sparse matrices on GPUs including BRO-ELL, BRO-COO, BRO-HYB, which perform compression on index data and help to speed up SpMV on GPUs through reduction of memory traffic ...compressed sparse ... See full document
10
Permonace Modeling of Pipelined Linear Algebra Architectures on ASIC
... The matrix–vector multiplication architecture defines a pipeline as a single multiply accumulate (MAC) unit ...The matrix operands be utilized once, yet the vector values can be used ... See full document
11
Optimizing Sparse Matrix Vector Multiplications on An ARMv8 based Many Core Architecture
... Abstract. Sparse matrix-vector multiplications (SpMV) are common in scientific and HPC applications but are hard to be ...956 sparse datasets and five mainstream sparse matrix ... See full document
12
Sparse Matrix Multiplication Using UPC Hoda El-Sayed and Eric Wright Department of Computer Science Bowie State University
... distribute matrix A and matrix B among the different processes which act as the ...from matrix A and columns from matrix B that are only accessible by the ...process. Matrix C will be ... See full document
5
A sparse multinomial probit model for classification
... estimating Σ directly and re-scaling [31]. To avoid the problem we adopt an identity covariance structure. This removes the need to estimate Σ at all, but the price of doing this is a reversion to the IIA constraint ... See full document
26
Fast Secure Matrix Multiplications over Ring-Based Homomorphic Encryption
... secure matrix multiplications (see Figure 1 below for an image of our ...computation, matrix-vector multiplication is proposed in HElib (see [15] for its ...the matrix-vector ... See full document
21
Application-Specific Memory Subsystem Benchmarking
... The parallel cores are tightly coupled on a single chip. By running concurrently, they yield better overall performance than the equivalent single cores, leading to the shift from sequential computing to parallel ... See full document
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