This is a DNN dataflow optimizer to find a optimal or approximately optimal dataflow for a particular DNN with a defined hardware configuration.

The goal of this optimizer are followings:

• First, this optimizer is to aim to find a close-to optimal configuration in order to minimize the latency and reduce the data traffic at the same time.
• Second, this optimizer explores different searching or optimization schemes, so that, we can show the trade-off between different optimization schemes.
• Third, this optimizer can automatically decompose deconvolutions in the DNN pipeline and apply different level optimization to deconvolutions

There are two main parts in this framework:

1. The overall framework to drive different optimizers.

• dnn_optimizer.py
• dataflow_search.py

2. The layer-level optimizers to optimize different

• layer_base_method.py
• layer_optimizer.py
• layer_exhaustive_searcher.py
• deconv_exhaustive_searcher.py

You can run the example dataflow optimization by runner.sh.

To use the dataflow optimizer, you can run for helping info:

 $ python dataflow_search.py -h 

By specify the configuration in the input option and a particular DNN network you want to optimize, the optimizer will return a dataflow scheme for you. The sample DNN networks are in the /dnns directory.

There are three parts consisted all the flags

1. input and output files

• --dnnfile : the actual dnn dataflow file to optimize.
• --outfile : the file to dump all the result.

2. options that are related to search options

• --static : to set the flag if static partition the buffer enable
• --split : enable to split the convolution kernel into small sub-kernels
• --combine : enable to combine the sub-kernels durting compute
• --model_type : DNN model convolution type: 2D or 3D.
• --ifmap : the ifmap dimemsion, order: [W H C]
• --ifmap3d : the ifmap dimemsion, order: [W H D C]
• --buffer_partition : the ifmap dimemsion, order: [I O W]
• --search_method : there are three search options: "Constrained", "Exhaustive", "Combined", the first one is to use constrained optimization, "Exhaustive" is a exhaustive search with the combination of DP. "Combine" is to use static partition to set initial guess values for constrained optimization and then use constrained optimization.

3. other hardware configurations

• --bufsize, in Btyes
• --memory_bandwidth, in GB/s
• --sa_size, Systolic array size"
• --bit_width, Bit Width of each value (typically, 8-bit, 16-bit, 32-bit)