Low Power and High-Throughput LUT-based Accelerator Architecture for Distributed CNN Inference at the Edge

Kurzfassung

Enabling distributed CNN inference at network's edge demands efficient solutions due to resource limitations and strict timing and energy constraints. LUT-based architectures, which aims at storing pre-computed neural network layers as LUTs, allow eliminating external memory and enabling fast, energy-efficient inference. Their use into an ecosystem for distributed CNN processing across multiple edge devices opens the door to novel applications in the field of future mobility and aerospace. In this paper, we present a flow using LUT-based accelerators for distributed CNN inference at edge.