Implementations of matrix multiplication via diffusion and reactions, thus eliminating the need for electronics, have been proposed as a stepping stone to realize molecular nano-neural networks (M3N).

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

Conclusion nvmath-python represents a significant advancement in leveraging NVIDIA’s powerful math libraries within Python environments. By fusing epilog operations with matrix multiplication, it ...

🚀 The feature, motivation and pitch 3D fp8 matrix multiplication can be useful for fp8 model with 3D matmul (it also can be used to improve accuracy of models with 2D fp8 quantized matrix ...

By separating huge dimensional matrix-matrix multiplication at a single computing node into parallel small matrix multiplications (with appropriate encoding) at parallel worker nodes, coded ...

EPFL researchers developed the first large-scale in-memory processor using 2D semiconductor materials, which might significantly reduce the energy footprint of the ICT sector.

EPFL researchers have created an energy-efficient in-memory processor using MoS 2, combining over 1000 transistors. This processor, which efficiently performs vector-matrix multiplication, represents ...

Researchers developed new insights into Avalanche Photodiodes (APDs) for enhanced optical communication and quantum information applications.