1 research outputs found
LOCAL: Low-Complex Mapping Algorithm for Spatial DNN Accelerators
Deep neural networks are a promising solution for applications that solve
problems based on learning data sets. DNN accelerators solve the processing
bottleneck as a domain-specific processor. Like other hardware solutions, there
must be exact compatibility between the accelerator and other software
components, especially the compiler. This paper presents a LOCAL (Low
Complexity mapping Algorithm) that is favorable to use at the compiler level to
perform mapping operations in one pass with low computation time and energy
consumption. We first introduce a formal definition of the design space in
order to define the problem's scope, and then we describe the concept of the
LOCAL algorithm. The simulation results show 2x to 38x improvements in
execution time with lower energy consumption compared to previous proposed
dataflow mechanisms