Abstract:
Moving sensor data and neural-network operands through conventional external-memory hierarchies can constitute a substantial fraction of the energy required for edge inference. Motivated by a three-tier near-sensor organization, this work evaluates memory localization using a controlled architecture-level study and an independent transistor-level communication-link analysis. Five convolution configurations are evaluated on …