From the 1 of 9 linked papers with an AI index.
9 papers
The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing
Stefan Scholze, Johannes Partzsch, Sebastian Höppner +27
In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an…
Simulation of Custom-Precision OCP MX Block Floating-Point Formats and Arithmetic
Maliha Islam, Mantas Mikaitis
The paper introduces MXsim v0.1, a MATLAB library that simulates OCP MX block floating-point formats and arithmetic, enabling researchers to experiment with custom-precision settin…
What is New in Stochastic Rounding: a Survey on Theory, Hardware, and Applications
El-Mehdi El Arar, Massimiliano Fasi, Silviu-Ioan Filip +1
Stochastic rounding (SR) is a probabilistic method used to round numbers to floating-point and fixed-point representations. In length summation, the worst-case error of SR grow…
Accurate Models of NVIDIA Tensor Cores
Faizan A. Khattak, Mantas Mikaitis
Matrix multiplication is a fundamental operation in both training of neural networks and inference. To accelerate matrix multiplication, Graphical Processing Units (GPUs) provide i…
Analysis of Floating-Point Matrix Multiplication Computed via Integer Arithmetic
Ahmad Abdelfattah, Jack Dongarra, Massimiliano Fasi +2
Ootomo, Ozaki, and Yokota [Int. J. High Perform. Comput. Appl., 38 (2024), p. 297-313] have proposed a strategy to recast a floating-point matrix multiplication in terms of integer…
Probabilistic Error Analysis of Limited-Precision Stochastic Rounding: Horner's Algorithm and Pairwise Summation
El-Mehdi El Arar, Massimiliano Fasi, Silviu-Ioan Filip +1
Stochastic rounding (SR) is a probabilistic rounding mode that mitigates errors in large-scale numerical computations, especially when prone to stagnation effects. Beyond numerical…