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 ...

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 ...

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.

Figure 1 The Tensor Unit is optimized for 64-bit RISC-V cores. Source: Semidynamics The Tensor Unit, built on top of the company’s Vector Processing Unit, leverages the existing vector registers to ...

Mathematics DeepMind AI finds new way to multiply numbers and speed up computers Matrix multiplication - where two grids of numbers are multiplied together - forms the basis of many computing ...

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.