Engheta and colleagues have now set their sights on vector–matrix multiplication, which is a vital operation for the artificial neural networks used in some artificial intelligence systems. The team ...

Sparse Matrix Multiplication October 1, 2015 by MichaelS Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the ...

Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the widely-used, memory-bound Sparse ...

According to Haerang Choi and colleagues at SK hynix, in a presentation at IEDM, matrix-vector multiplication accounts for 90% of the response phase workload. [3] Because it requires less than one ...

Eigenvalues and Eigenvectors: A basic knowledge of eigenvalues and eigenvectors of matrices, coupled with an ability to perform matrix-vector multiplication. Systems of First Order Linear Differential ...

However, the traditional incoherent matrix-vector multiplication method focuses on real-valued operations and does not work well in complex-valued neural networks and discrete Fourier transforms.