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: Efficient matrix multiplication is a crucial issue of AI, signal processing, and computing systems. This paper proposes an optimized matrix multiplication architecture, which incorporates ...
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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