Changes Since Last Release

Major Changes

Adds three new encodings: (i) TriangleWave, (ii) SphericalHarmonics, (iii) Composite
Pitched pointers are now used to parameterize inputs and outputs of all encodings.
- This feature enables a new Composite encoding that can apply basic encodings to different subsets of input dimensions.
- This also removes the distinction of "encoded dims" vs. "passthrough_dims". The old behavior of passing through certain dimensions can be achieved by composing with the Identity encoding.
tiny-cuda-nn no longer depends on cuRAND and instead uses an implementation of the PCG32 random number generator (derived from https://github.com/wjakob/pcg32) for all randomness.
Activation code has been centralized within and across CUTLASS components. All neural network implementations now support all activation functions (except for the ResNet, which still only supports ReLU activations in its hidden layers).

Installed GPUs are now correctly automatically detected and targeted by CMake.
Samples and benchmarks can now be disabled when tiny-cuda-nn is used as a submodule.
The required CUDA version has been relaxed. Future plans include compatibility with CUDA 10.2