Abstract: Graph convolutional networks (GCNs) are emerging neural network models designed to process graph-structured data. Due to massively parallel computations using irregular data structures by ...
Abstract: Matrix operators are fundamental to various applications, particularly in deep learning. While early models relied on dense operations, techniques like pruning have introduced sparsity, ...
Note: The project requires an NVIDIA GPU with CUDA support. The code is tested on Ubuntu 20.04 with CUDA 12.1 and PyTorch 2.3.1. Windows system is strongly ...
D-Matrix says its chips can run inference workloads 10 times faster and using five times less energy than a standalone graphics processing unit from Nvidia. Like Cerebras, D-Matrix is trying to prove ...
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