coreylammie/MemTorch: 1.1.2 Release

Added

1. C++ and CUDA bindings for memtorch.bh.crossbar.Tile.tile_matmul.

Using an NVIDIA GeForce GTX 1080, a tile shape of (25, 25), and two tensors of size (500, 500), the runtime of tile_matmul without quantization support is reduced by 2.45x and 5.48x, for CPU-bound and GPU-bound operation, respectively. With an ADC resolution of 4 bits and an overflow rate of 0.0, the runtime of tile_matmul with quantization support is reduced by 2.30x and 105.27x, for CPU-bound and GPU-bound operation, respectively.

Implementation Runtime Without Quantization Support (s) Runtime With Quantization Support (s) Pure Python (Previous) 6.917784 27.099764 C++ (CPU-bound) 2.822265 11.736974 CUDA (GPU-bound) 1.262861 0.2574267

1. Eigen integration with C++ and CUDA bindings.
2. Additional unit tests.

Enhanced

1. Modularized C++ and CUDA quantize bindings.
2. Enhanced functionality of naive_progam and added additional input arguments to dictate logic for stuck devices.

Fixed

1. Removed debugging code from naive_progam.