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coreylammie/MemTorch: 1.1.2 Release

Authors: Corey Lammie; Nikhil Garg;

coreylammie/MemTorch: 1.1.2 Release

Abstract

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.

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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).
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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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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