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 ...

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.

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 ...

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 ...

Researchers upend AI status quo by eliminating matrix multiplication in LLMs Running AI models without floating point matrix math could mean far less power consumption.