원래 이름 기록: The George Washington University

Emerging applications utilize numerous Deep Neural Networks (DNNs) to address multiple tasks simultaneously. As these applications continue to expand, there is a growing need for off-chip memory access optimization and innovative architectures that can adapt to diverse computation, memory, and communication requirements of various DNN models. To address these challenges, Versa-DNN is a versatile DNN accelerator that can provide efficient computation, memory, and communication support for the simultaneous execution of multiple DNNs. Versa-DNN features three unique designs: a flexible off-chip memory access optimization strategy, adaptable communication fabrics, and a communication and computational aware scheduling algorithm. The off-chip memory optimization strategy improves performance and energy efficiency by increasing hardware utilization, eliminating excess data duplication, and reducing off-chip memory accesses. The adaptable communication fabrics include distributed buffers, processing elements, and a flexible Network-on-Chip (NoC), which can dynamically morph and fission to support distinct communication and computation needs for simultaneously running DNN models. Furthermore, Versa-DNN has a scheduling policy which manages the simultaneous execution of multiple DNN models with improved performance and energy efficiency.