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 ...

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

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 ...

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 ...

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.

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 ...

Matrix multiplication provides a series of fast multiply and add operations in parallel, and it is built into the hardware of GPUs and AI processing cores (see Tensor core). See compute-in-memory.

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 ...

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.