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AssigneeThe Governing Council of the University of Toronto
InventorsAndreas Moshovos, Alberto Delmas Lascorz, Zisis Poulos, Dylan Malone Stuart, Patrick Judd, Sayeh Sharify, Mostafa Mahmoud, Milos Nikolic, Kevin Chong Man Siu, Jorge Albericio
Estimating the term…
Described is a neural network accelerator tile for exploiting input sparsity. The tile includes a weight memory to supply each weight lane with a weight and a weight selection metadata, an activation selection unit to receive a set of input activation values and rearrange the set of input activation values to supply each activation lane with a set of rearranged activation values, a set of multiplexers including at least one multiplexer per pair of activation and weight lanes, where each multiplexer is configured to select a combination activation value for the activation lane from the activation lane set of rearranged activation values based on the weight lane weight selection metadata, and a set of combination units including at least one combination unit per multiplexer, where each combination unit is configured to combine the activation lane combination value with the weight lane weight to output a weight lane product.
A neural network accelerator tile for exploiting input sparsity defining a set of weight lanes and a set of activation lanes, each weight lane corresponding to an activation lane, the tile comprising:
An accelerator tile, comprising:
Dependencies are inferred from the claim text.