Mixed-Precision In- Memory Computing. arXiv preprint arXiv:1701.04279.Le Gallo M, Sebastian A, Mathis R, Manica M, Tuma T, Bekas C, Curioni A and Eleftheriou E 2017 arXiv.org arXiv:1701.04279 (Preprint 1701.0427
Here we introduce the concept of mixed-precision in-memory computing, which combines a von Neumann machine with a computational memory unit. In this hybrid system, the computational memory unit performs the bulk of a computational task, while the von Neumann machine implements a backward method to...
Mixed-Precision In-Memory Computing Manuel Le Gallo*,1, 2 Abu Sebastian*,1 Roland Mathis,1 Matteo Manica,1, 2 Heiner Giefers,1 Tomas Tuma,1 Costas Bekas,1 Alessandro Curioni,1 and Evangelos Eleftheriou1 1)IBM Research - Zurich, 8803 Ru¨schlikon, Switzerland 2)ETH Zurich, 8092 Zurich,...
Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeting this application. A computational memory unit with nanoscale resistive memory devices organized in crossbar arrays could store the synaptic weights in their conductance...
Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital,...
Consider the computing hardware loadout and enable hardware in an appropriate manner. For example, dedicate one GPU to storing memory for the training session, and enable features like CUDA, a parallel computing technology and programming model for GPU acceleration. ...
As these laws break down due to technologicallimits, a radical departure from the processor-memory dichotomy is needed to circumvent the limitations of today’scomputers. ‘Memcomputing’ is a promising concept in which the physical attributes and state dynamics of nanoscaleresistive memory devices ...
while dramatically decreasing the required memory, application runtime, and system power consumption. There’s a growing number of examples where researchers are leveraging GPU Tensor Cores and mixed-precision computing to accelerate traditional FP64-based scientific computing applications by up to 25 tim...
That is, Tensor Cores cannot run at full throughput because memory bandwidth will be the limiting factor. A kernel with sufficient arithmetic intensity to allow full Tensor Core throughput is compute-bound. It is possible to increase arithmetic intensity both in model implementation and model ...
Automatic Mixed Precision feature is available both in native MXNet (1.5 or later) and inside the MXNet container (19.04 or later) on NVIDIA NGC container registry. To enable the feature, add the following lines of code to your existing training script: ...