Space‐address decoupled scratchpad memory management for neural network accelerators

2020 ◽  
Author(s):  
Zhenxing Zhang ◽  
Shiyan Sun ◽  
Xunyu Chen ◽  
Tian Zhi ◽  
Qi Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Memory Management ◽  
Scratchpad Memory

scholarly journals Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

2019 ◽  
Vol 9 (2) ◽  
pp. 608-612
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Storage ◽  
Memory Management ◽  
Research Work ◽  
Dynamic Resource Allocation ◽  
Mobile Platform ◽  
Dynamic Resource

Memory management is very essential task for large-scale storage systems; in mobile platform generate storage errors due to insufficient memory as well as additional task overhead. Many existing systems have illustrated different solution for such issues, like load balancing and load rebalancing. Different unusable applications which are already installed in mobile platform user never access frequently but it allocates some memory space on hard device storage. In the proposed research work we describe dynamic resource allocation for mobile platforms using deep learning approach. In Real world mobile systems users may install different kind of applications which required ad-hoc basis. Such applications may be affect to execution performance of system as well space complexity, sometime they also affect another runnable applications performance. To eliminate of such issues, we carried out an approach to allocate runtime resources for data storage for mobile platform. When system connected with cloud data server it store complete file system on remote Virtual Machine (VM) and whenever a single application required which immediately install beginning as remote server to local device. For developed of proposed system we implemented deep learning base Convolutional Neural Network (CNN), algorithm has used with tensorflow environment which reduces the time complexity for data storage as well as extraction respectively.

CASA: Contention-Aware Scratchpad Memory Allocation for Online Hybrid On-Chip Memory Management

2014 ◽  
Vol 33 (12) ◽  
pp. 1806-1817 ◽  
Author(s):  
Da-Wei Chang ◽  
Ing-Chao Lin ◽  
Yu-Shiang Chien ◽  
Chin-Lun Lin ◽  
Alvin W.-Y Su ◽  
...  
Keyword(s):  
Memory Management ◽  
Memory Allocation ◽  
Scratchpad Memory ◽  
On Chip

scholarly journals Core Working Set Based Scratchpad Memory Management

2011 ◽  
Vol E94-D (2) ◽  
pp. 274-285 ◽  
Author(s):  
Ning DENG ◽  
Weixing JI ◽  
Jiaxin LI ◽  
Qi ZUO ◽  
Feng SHI
Keyword(s):  
Memory Management ◽  
Scratchpad Memory ◽  
Working Set

Dynamic scratchpad memory management for code in portable systems with an MMU

2008 ◽  
Vol 7 (2) ◽  
pp. 1-38 ◽  
Author(s):  
Bernhard Egger ◽  
Jaejin Lee ◽  
Heonshik Shin
Keyword(s):  
Memory Management ◽  
Scratchpad Memory

A Novel Adaptive Scratchpad Memory Management Strategy

2009 ◽  
Author(s):  
Ning Deng ◽  
Weixing Ji ◽  
Jiaxin Li ◽  
Feng Shi ◽  
Yizhuo Wang
Keyword(s):  
Management Strategy ◽  
Memory Management ◽  
Scratchpad Memory

Efficient Scratchpad Memory Management for Mobile Multimedia Application

2013 ◽  
Vol 748 ◽  
pp. 932-935
Author(s):  
Ze Yu Zuo ◽  
Wei Hu ◽  
Rui Xin Hu ◽  
Heng Xiong ◽  
Wen Bin Du ◽  
...  
Keyword(s):  
Power Consumption ◽  
Mobile Devices ◽  
Memory Management ◽  
Multimedia Applications ◽  
Access Time ◽  
Management Approach ◽  
Mobile Multimedia ◽  
Scratchpad Memory ◽  
Mobile Multimedia Application ◽  
On Chip

Mobile devices have been popular in recent years and the proliferation of mobile devices inspires the interest in mobile multimedia applications. However, memory is always the bottleneck in the traditional memory hierarchy. Scratchpad memory (SPM) is a promising on-chip SRAM to solve such problem. It has faster access time and less power-consumption compared to cache and off-chip memory. In this paper, we propose the efficient scratchpad memory management approach for mobile multimedia applications. SPM is partitioned for the assignment of the slices of the applications based on the profiling and the recorded history. Through the use of SPM, the memory footprint of mobile multimedia applications will be reduced for better performance and less power-consumption. The experimental results show that our approach is able to significantly reduce the power consumption and improve the performance of mobile multimedia applications.

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