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Youxu Lin

dblp:357/2922 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 56% Reconfigurable computing and FPGAs · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Reconfigurable computing and FPGAs
coarse-grained reconfigurable architecture
0.712023
RMP-MEM: A HW/SW Reconfigurable Multi-Port Memory Architecture for Multi-PEA Oriented CGRA · DAC 2023
Memory systems › memory architecture
multi-ported memory
0.712023
RMP-MEM: A HW/SW Reconfigurable Multi-Port Memory Architecture for Multi-PEA Oriented CGRA · DAC 2023
Memory systems
memory architecture
0.212023
RMP-MEM: A HW/SW Reconfigurable Multi-Port Memory Architecture for Multi-PEA Oriented CGRA · DAC 2023

Methods — techniques the papers use, named apart from their topics

hardware-software co-design · 0.7
YearPublicationVenuePosition
2023 RMP-MEM: A HW/SW Reconfigurable Multi-Port Memory Architecture for Multi-PEA Oriented CGRA
abstract
Coarse-Grained Reconfigurable Architecture (CGRA), especially the one with multiple parallelized Processing Element Arrays (PEA), possesses flexible programmability and high parallel computational efficiency, which relies upon an efficient memory architecture to deliver the corresponding computing power. Multi-PEA oriented CGRA allows for mapping various applications and thus demands a flexible memory to adapt to the ever-changing workloads, whose parallel access also requires an efficient multi-port memory. However, the existing memory designs for CGRA are hard to satisfy those requirements since conventional rigid memories fail to provide the desired flexibility due to fixed structure, and traditional multi-port designs are impractical due to large overhead. Therefore, this paper proposes a hardware/software (HW/SW) hybrid reconfigurable multi-port memory architecture (RMP-MEM) with an instructive analysis for the multi-PEA oriented CGRA. RMP-MEM supports adaptive memory partition and programmer-defined access modes to adapt the different features of memory accesses. Also, RMP-MEM achieves an efficient multi-port implementation by a partially shared mechanism. Furthermore, the microarchitecture of RMP-MEM is optimized multi-directionally, resulting in a significant performance gain. The experimental results indicate that RMP-MEM reduces the parallel access latency by 81.1% and exhibits 28.3% energy efficiency improvement compared to prior designs.
Qidie Wu, Jiangyuan Gu, Youxu Lin, Boxiao Han, Hongjun He, Yang Hu 0001, Leibo Liu, Shaojun Wei, Shouyi Yin
DAC3