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 ...

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.

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 ...

Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...

A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...

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 ...

Pouring over data When I gave you the rule concerning matrix multiplication, I made no pretense that it was easy or intuitively obvious. On the contrary, I told you that although it seemed crazy, it ...