guide for intel dla

本文介绍了DLA(深度学习加速器)的编程模型,包括子图的构成方式、流水线并行性和顺序执行等特性。此外,还详细阐述了通过流缓冲区进行数据通信和地址分配的不同方式。

 

1.net seen by dla
net graph=subgraph1+subgraph2+subgraph3+...
subgraph=conv+relu+norm+maxpool+fullyconn
#every unit of subgraph is optional
2.dla construct
#         stream buffer(double buffer):        
#|<-----------------buffer 1<------------------|
#|<-----------------buffer 2<------------------|
#|         stream pu(processing unit):         |
#|---->conv->relu->norm->maxpool->fullyconn--->|
#@ DLA Can Execute Many Neural Networks
#@ DLA – Pipeline Parallelism of Subgraph Nodes
#@ DLA – Sequentially Executes a Sequence of Subgraphs
3.dla programming model
3.1 buffer communicate
transfer data includes buffer_image,buffer_conv_filter,buffer_conv_bias:
  _______       ___________       ___________                               ________  
 |       |<--->| ddr       |<--->|           |<--->buffer_image       <--->|        |   
 | host  |<--->| device    |<--->| subgraph0 |<--->buffer_conv_filter <--->| stream |   
 |       |<--->| buffer0~3 |<--->| subgraph1 |<--->buffer_conv_bias   <--->| buffer |    
 |_______|<--->|___________|<--->|___________|<--->buffer_conv_feature<--->|________|     

3.1.1 communicate via streambuffer
host<->ddr<->subgraph<->stream buffer(onchip, limited size)
3.1.2 communicate via ddr
host<->ddr<->subgraph
3.2 buffer addressing
3.2.1 manual addressing
#@ The user chooses where the output goes: DDR or StreamBuffer.
#@ The user provides ALL the addresses for DDR and StreamBuffer.
3.2.2 auto addressing
#@ The user chooses only which inputs/outputs go to DDR.
#@ The user provides addresses only for DDR + subgraph data dependences.
   Intermediate data location/address between subgraphs is unspecified.
   DLA library determines the intermediate data addresses.
   Subgraphs may be sliced to fit in stream buffer.
#@ There are limitations of the type of graphs that can be handled.
#@
#@

转载于:https://www.cnblogs.com/wordchao/p/8203973.html

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