Motivated by the challenge of investigating the reproducibility of spiking neural network simulations, we have developed the Arpra library: an open source C library for arbitrary precision range analysis based on the mixed Interval Arithmetic (IA)/Affine Arithmetic (AA) method. Arpra builds on this method by implementing a novel mixed trimmed IA/AA, in which the error terms of AA ranges are minimised using information from IA ranges. Overhead rounding error is minimised by computing intermediate values as extended precision variables using the MPFR library. This optimisation is most useful in cases where the ratio of overhead error to range width is high. Three novel affine term reduction strategies improve memory efficiency by merging affi...
The precision used in an algorithm affects the error and performance of individual computations, the...
An important aspect of modern automation is machine learning. Specifically, neural networks are used...
Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution...
Motivated by the challenge of investigating the reproducibility of spiking neural network simulation...
This study explores how numerical error occurs in simulations of spiking neural network models, and ...
Approximate Computing (AxC) techniques allow trade-off accuracy for performance, energy, and area re...
Preprint submitted to Elsevier. It has not been certified by peer review.Reduced precision number fo...
International audienceDeep Neural Networks (DNN) represent a performance-hungry application. Floatin...
Several hardware companies are proposing native Brain Float 16-bit (BF16) support for neural network...
Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successf...
AbstractComputer solutions of scientific and engineering problems involve several sources of floatin...
An emerging area of research is to automatically compute reasonably accurate upper bounds on numeric...
International audienceGraphics Processing Units (GPUs) offer the possibility to execute floating-poi...
We present a detailed study of roundoff errors in probabilistic floating-point computations. We deri...
Approximate Computing (AxC) allows reducing the accuracy required by the user and the precision prov...
The precision used in an algorithm affects the error and performance of individual computations, the...
An important aspect of modern automation is machine learning. Specifically, neural networks are used...
Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution...
Motivated by the challenge of investigating the reproducibility of spiking neural network simulation...
This study explores how numerical error occurs in simulations of spiking neural network models, and ...
Approximate Computing (AxC) techniques allow trade-off accuracy for performance, energy, and area re...
Preprint submitted to Elsevier. It has not been certified by peer review.Reduced precision number fo...
International audienceDeep Neural Networks (DNN) represent a performance-hungry application. Floatin...
Several hardware companies are proposing native Brain Float 16-bit (BF16) support for neural network...
Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successf...
AbstractComputer solutions of scientific and engineering problems involve several sources of floatin...
An emerging area of research is to automatically compute reasonably accurate upper bounds on numeric...
International audienceGraphics Processing Units (GPUs) offer the possibility to execute floating-poi...
We present a detailed study of roundoff errors in probabilistic floating-point computations. We deri...
Approximate Computing (AxC) allows reducing the accuracy required by the user and the precision prov...
The precision used in an algorithm affects the error and performance of individual computations, the...
An important aspect of modern automation is machine learning. Specifically, neural networks are used...
Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution...