  The inherent diversity of computation types within individual Deep Neural
Network (DNN) models imposes a corresponding need for a varied set of
computation units within hardware processors. This diversity poses a
significant constraint on computation efficiency during the execution of
different neural networks. In this study, we present NeuralMatrix, a framework
that transforms the computation of entire DNNs into linear matrix operations.
This transformation seamlessly enables the execution of various DNN models
using a single General-Purpose Matrix Multiplication (GEMM) accelerator.
Extensive experimental results spanning different DNN models demonstrate that
our approach preserves network accuracy while providing both generality and
application-specific levels of computation efficiency. This allows a broad
spectrum of DNN models to be executed using a single GEMM accelerator,
eliminating the need for additional special function units.
