下图是TFLite对MobileNet v2做memory optimization前后的memory使用情况对比[1],可以看到,优化之后,整个网络的intermediate tensor的memory占用降低到原来的1/4。 Before memory optimization, intermediate tensor takes about ~26MB After memory optimization, intermediate tensor takes only about ~7MB 接下来我们介绍memo...
In addition, we designed and implemented an intelligent prefetching algorithm (from CPU memory to GPU) that achieves the highest processing throughput while sustaining a large mini-batch size. We successfully implemented our optimization techniques on TensorFlow, and performed extensive experiments in ...
Memory Issues in Deep LearningDeep learning models' size and complexity have recently increased. Such massive models need a vast amount of memory to train. DeepSpeed, developed by Microsoft as a deep learning optimization library, presented powerful solutions to the challenges....
The present application discloses a processor video memory optimization method and apparatus for deep learning training tasks, and relates to the technical field of artificial intelligence. In the method, by determining an optimal path for transferring a computing result, the computing result of a fir...
Opens in a new tab Publication Downloads DeepSpeed February 12, 2020 DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 10x Larger Models 5x Faster Training Minimal Code Change DeepSpeed can train DL models with over a hund...
HDC is well suited to address several learning tasks in IoT systems as: (i) HDC is computationally efficient and amenable to hardware level optimization30,31,32, (ii) it supports single-pass training with no back-propagation or gradient computation, (iii) HDC offers an intuitive and human-...
Hardware-aware neural architecture search (HW-NAS) is an efficient tool in hardware–software co-design, and it can be combined with other architecture-level and system-level optimization techniques to design efficient in-memory computing (IMC) hardware for deep learning accelerators. ...
Researchers have also observed the need for memory cost modeling for DNN memory optimization and planning by analyzing the computation graph [26, 38, 49]. Unlike these work, DNNMem focuses on memory estimation for DL models. 7 CONCLUSION In this paper, we have presented DNNMem, an accurate ...
As a result, graph learning5 is quickly standing out in many real-world applications such as the prediction of chemical properties of molecules for drug discovery6, recommender systems of social networks7 and combinatorial optimization for design automation8. In the era of Big Data and the ...
Optimization 1: Direct access to system memory (zero-copy) As an alternative to moving memory pages from system memory to GPU memory over the interconnect, you can also directly access the pinned system memory from the GPU. This memory allocation methodology is also known as zero-copy memory....