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The inherent diversity of computation types within individual deep neuralnetwork (DNN) models necessitates a corresponding variety of computation unitswithin hardware processors, leading to a significant constraint on computationefficiency during neural network execution. In this study, we introduceNeuralMatrix, a framework that transforms the computation of entire DNNs intolinear matrix operations, effectively enabling their execution with onegeneral-purpose matrix multiplication (GEMM) accelerator. By surmounting theconstraints posed by the diverse computation types required by individualnetwork models, this approach provides both generality, allowing a wide rangeof DNN models to be executed using a single GEMM accelerator andapplication-specific acceleration levels without extra special function units,which are validated through main stream DNNs and their variant models.

NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference