Parallel DNN Inference Framework Leveraging a Compact RISC-V ISA-based Multi-core System
Aug 13, 20201 views
RISC-V is an open-source instruction set and now has been examined as a universal standard to unify the heterogeneous platforms.,However, current research focuses primarily on the design and fabrication of general-purpose processors based on RISC-V, despite the,fact that in the era of IoT (Internet of Things), the fusion of heterogeneous platforms should also take application-specific processors,into account. Accordingly, this paper proposes a collaborative RISCV multi-core system for Deep Neural Network (DNN) accelerators.,To the best of our knowledge, this is the first time that a multi-core,scheduling architecture for DNN acceleration is formulated and,RISC-V is explored as the ISA of a multi-core system to bridge,the gap between the memory and the DNN Processor in order to,increase the entire system throughput. The experiment realizes a,four-stage design of the RISC-V core, and further reveals that a,multi-core design along with an appropriate scheduling algorithm,can efficiently decrease the runtime and elevate the throughput.,Moreover, the experiment also provides us with a constructive,suggestion regarding the ideal proportion of the cores to Process,Engines (PE), which provides us with significant assistance in building highly efficient AI System-on-Chips (SoCs) in resource-aware,situations.