Design and Implementation of a RISC-V SoC with Custom DSP Accelerators for Edge Computing
원문 초록
This paper presents a comprehensive analysis of the RISC-V instruction set architecture, focusing on its modular design, implementation challenges, and performance characteristics. We examine the RV32I base instruction set with extensions for multiplication (M) and atomic operations (A). Through cycle-accurate simulation of a pipelined implementation, we evaluate performance metrics including CPI (cycles per instruction) and power efficiency. Our results demonstrate RISC-V's advantages in embedded systems and its scalability for custom accelerators. Comparative analysis shows a 17% reduction in power consumption compared to ARM Cortex-M0 implementations in similar process nodes. The open-standard nature of RISC-V provides significant flexibility for domain-specific optimizations.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning
Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks
PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.