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PULP-TrainLib - Training Library sources

PULP-TrainLib is organized as follows:

  • include/: contains the header files. The structures to configure the support functions and their function headers are defined in pulp_train_utils_fpXX.h. The layer configuration structures and primitives are defined in each layers' .h file.
  • sources/: contains the body of the functions.

To use PULP-TrainLib in your project, include pulp_train.h.

Structure definitions and data types

PULP-TrainLib is designed to work with different data types. For each type, PULP-TrainLib provides a set of files (for example, pulp_xxx_fp32.h contains fp32 primitives).

Most of PULP-TrainLib's primitives and functions require specific data structures as arguments, passing the operands implicitly (as void* args). For each data type, the data structures are defined inside pulp_train_utils_fpxx.h (for support functions) and inside the related file for primitives (e.g. pulp_linear_fp32.h for a fp32 fully-connected layer).

Training primitives

The same naming convention holds for each DNN layer. In each layers' files, the Forward and Backward functions are defined for each of the available data types. E.g: the primitives of a CONV2D layer in fp16 are defined inside pulp_conv2d_fp16.h and pulp_conv2d_fp16.c. Inside each of these files, you can find both the forward and the backward functions. The same holds for the activation functions and others.

Define the fp16 format

The PULP Platform supports multiple fp16 data formats. To select the one you need, please refer to pulp_train_defines.h. In this file, you can select either float16 (fp16 1-5-10 - Sign-Exponent-Mantissa), or float16alt (Bfloat16 1-8-7).

Other general defines

pulp_train_defines.h contains useful defines and macros used to support the library.

Building a DNN training workload

To automatically generate the C deployment code of your DNN model, make use of the TrainLib Deployer.

To manually develop the deployment code for your on-device learning task, you need to:

  • define the arrays which will contain tensor data and gradients;
  • define the sizes of each tensor;
  • for each tensor, define a blob structure to wrap the data, gradient and sizes;
  • write the code for forward and backward steps, comprising the loss and optimizer. Note that the optimizer has to be applied to all the weight tensors of each layer;
  • schedule the training task from PULP's fabric controller to PULP's cluster to exploit parallel computations on NUM_CORES.

Please refer to the tests/ folder for examples.