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 ...

The aim of this study was to integrate the simplicity of structured sparsity into existing vector execution flow and vector processing units (VPUs), thus expediting the corresponding matrix ...

SpMV: Sparse Matrix–Vector Multiplication, a core operation in many numerical algorithms where a sparse matrix is multiplied by a vector.

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.

Matrix-vector multiplication can be used to calculate any linear transform. For vector-vector operations, Lenslet includes in the EnLight256 silicon a vector processing unit (VPU) that does operations ...

It is compatible across many different compilers, languages, operating systems, linking, and threading models. In particular, the Intel MKL DGEMM function for matrix-matrix multiplication is highly ...

The multiplication of two rectangular number arrays, known as matrix multiplication, plays a crucial role in modern AI models, including speech and image recognition, and is used by chatbots from all ...

The most widely used matrix-matrix multiplication routine is GEMM (GEneral Matrix Multiplication) from the BLAS (Basic Linear Algebra Subroutines) library. And these days it can be found being used in ...

DeepMind breaks 50-year math record using AI; new record falls a week later AlphaTensor discovers better algorithms for matrix math, inspiring another improvement from afar.