Views provided by UsageCounts
Added 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 Eigen integration with C++ and CUDA bindings. Additional unit tests. Enhanced Modularized C++ and CUDA quantize bindings. Enhanced functionality of naive_progam and added additional input arguments to dictate logic for stuck devices. Fixed Removed debugging code from naive_progam.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 2 |

Views provided by UsageCounts