A RISC-V Multi-Core Accelerator for Flexible High-Performance In-Network Computing

In this paper, we propose a universal multi-core processor architecture tailored for flexible and high-performance in-network computing scenarios. With the rapid increase in network speed and the emergence of in-network computing applications, the number of packet processing engines in packet processors has surged, leading to a corresponding increase in communication demands between these engines. This, in turn, has posed new challenges to the multi-core design of packet processors. We analyze the differences in inter-core traffic between conventional multi-core processors and packet processors and design a hybrid ring-similar interconnection architecture. This architecture employs a novel node layout and introduces unidirectional channels to eliminate conflicts in the network. Eliminating conflicts reduces per-hop latency and contention latency, while leveraging the ring-similar topology minimizes serialization latency. Additionally, in response to the demands of in-network computing, we have designed a novel packet processing engine. This core extends the relevant instruction set and, by employing blocking multithreading and multiple issue techniques, is capable of supporting both SIMD and MIMD. This significantly improves the IPC when executing in-network computing tasks. The experimental results demonstrate that our design achieves significant improvements in throughput and latency for various in-network computing applications. In terms of area efficiency, it shows improvements ranging from 1.9 to 35.3 times compared to the current state-of-the-art across different applications.

Authors

Institutions

Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1109/icccs65393.2025.11069946
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A RISC-V Multi-Core Accelerator for Flexible High-Performance In-Network Computing

Chengliang Yin, Qingyu Zhang, Yanhua Lv
Software-Defined Networks and 5G
article

A RISC-V Multi-Core Accelerator for Flexible High-Performance In-Network Computing

Chengliang Yin, Qingyu Zhang, Yanhua Lv
article en

Abstract

In this paper, we propose a universal multi-core processor architecture tailored for flexible and high-performance in-network computing scenarios. With the rapid increase in network speed and the emergence of in-network computing applications, the number of packet processing engines in packet processors has surged, leading to a corresponding increase in communication demands between these engines. This, in turn, has posed new challenges to the multi-core design of packet processors. We analyze the differences in inter-core traffic between conventional multi-core processors and packet processors and design a hybrid ring-similar interconnection architecture. This architecture employs a novel node layout and introduces unidirectional channels to eliminate conflicts in the network. Eliminating conflicts reduces per-hop latency and contention latency, while leveraging the ring-similar topology minimizes serialization latency. Additionally, in response to the demands of in-network computing, we have designed a novel packet processing engine. This core extends the relevant instruction set and, by employing blocking multithreading and multiple issue techniques, is capable of supporting both SIMD and MIMD. This significantly improves the IPC when executing in-network computing tasks. The experimental results demonstrate that our design achieves significant improvements in throughput and latency for various in-network computing applications. In terms of area efficiency, it shows improvements ranging from 1.9 to 35.3 times compared to the current state-of-the-art across different applications.

University of Electronic Science and Technology of China (CN), Shanghai Micro Electronics Equipment (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 81%
Software-Defined Networks and 5G
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.