text: 49,111,1163794283515785217,1104703454,Francesco Silvestri,"Tensor architectures (like @GoogleAI TPU and @nvidia TC) have been developed for accelerating Deep Learning applications. But can we exploit their computational power for other problems? We study this question in <LINK>. The heart of tensor architectures is a hardware circuit for multiplying small and dense matrices. We propose a computational model that captures this feature, and then design algorithms accelerating some linear algebra problems by exploiting tensor architectures. Joint work with @Flav1oV.",https://arxiv.org/abs/1908.06649,"To respond to the need of efficient training and inference of deep neural networks, a plethora of domain-specific hardware architectures have been introduced, such as Google Tensor Processing Units and NVIDIA Tensor Cores. A common feature of these architectures is a hardware circuit for efficiently computing a dense matrix multiplication of a given small size. In order to broaden the class of algorithms that exploit these systems, we propose a computational model, named the TCU model, that captures the ability to natively multiply small matrices. We then use the TCU model for designing fast algorithms for several problems, including matrix operations (dense and sparse multiplication, Gaussian Elimination), graph algorithms (transitive closure, all pairs shortest distances), Discrete Fourier Transform, stencil computations, integer multiplication, and polynomial evaluation. We finally highlight a relation between the TCU model and the external memory model. ",A Computational Model for Tensor Core Units,2,"['Tensor architectures (like @GoogleAI TPU and @nvidia TC)  have been developed for accelerating Deep Learning applications. But can we exploit their computational power for other problems? We study this question in <LINK>. The heart of tensor architectures is', 'a hardware circuit for multiplying small and dense matrices. We propose a computational model that captures this feature, and then design algorithms accelerating some linear algebra problems by exploiting tensor architectures. Joint work with @Flav1oV.']",19,08,510