pulp-trainlib-v0.4

G1) Conv2D, Fully-Connected layers - FP32 and FP16
G2) DepthWise Convolution, PointWise Convolution (no stride, padding, biases) - FP32 and FP16
G3) Max and Average Pooling
G4) ReLU activation
G5) Gradient Descent Optimizer
G6) MSELoss and CrossEntropyLoss
G7) Multi-Head Self Attention (FP32, FP16) and RNN (FP32) with enhanced
Check README.md for more details.

pulp-trainlib-v0.3

Release v0.3 of PULP-TrainLib with stable learning primitives in FP32 and FP16.

General features:

G1) DepthWise Convolution, PointWise Convolution, Conv2D, Fully-Connected layers (no pad, no stride, no biases) - FP32 and FP16
G2) Max and Average Pooling
G3) ReLU activation
G4) Gradient Descent Optimizer
G5) MSELoss and CrossEntropyLoss
G6) Multi-Head Self Attention (FP32, FP16) and RNN (FP32) with enhanced 
Check README.md for more details.

Also, the TrainLib_Deployer supports G1-5 features and layers.

pulp-trainlib-v0.2

Release v0.2 of PULP-TrainLib with stable learning primitives in FP32 and FP16.

General features:

G1) DepthWise Convolution, PointWise Convolution, Conv2D, Fully-Connected layers (no pad, no stride, no biases) - FP32 and FP16
G2) Max and Average Pooling
G3) ReLU activation
G4) Gradient Descent Optimizer
G5) MSELoss and CrossEntropyLoss
G6) Multi-Head Self Attention (FP32, FP16) and RNN (FP32)
Check README.md for more details.

Also, the TrainLib_Deployer supports G1-5 features.

pulp-trainlib-v0.1

First release of PULP-TrainLib with stable learning primitives in FP32 and FP16.

General features:

• G1) DepthWise Convolution, PointWise Convolution, Conv2D, Fully-Connected layers (no pad, no stride, no biases) - FP32 and FP16
• G2) Max and Average Pooling
• G3) ReLU activation
• G4) Gradient Descent Optimizer
• G5) MSELoss and CrossEntropyLoss
• G6) Multi-Head Self Attention and RNN - FP32
  Check README.md for more details.

Also, the TrainLib_Deployer supports G1-5 features.