Memory Admission and Result-Retention Sizing in an RV32IMC FPGA Soft Processor

In FPGA soft processors with shared backing block RAM, memory admission can stall a multiplication after its product has been prepared. We study how admission timing changes result retention in PaceRV, our board-tested five-stage RV32IMC core, using fixed binaries and matched dynamic instructions. Offering data requests one edge earlier reduces geometric-mean Embench cycles by 18.2% and retention opportunities from 375,063 to 4,373. Admission and retention cycle gains are conditional and non-additive. Under slower reads, a 99% capture target selects an 18-edge validity window with registered requests and 28 edges with early requests, although no multiplication matched within both measurement intervals waits longer. A predeclared sizing rule predicts opportunity recovery exactly in all finite tests, including kernels excluded from model development. Independent timing analysis closely estimates execution-cycle savings. The results support choosing admission first, then deciding whether to extend validity and sizing it against an explicit capture or missed-opportunity objective.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-11-02
DOI
https://doi.org/10.48550/arxiv.2611.02974
Primary Topic
Embedded Systems Design Techniques
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Memory Admission and Result-Retention Sizing in an RV32IMC FPGA Soft Processor

Shuyu Xu, Yinfeng Zhou, Chenglei Peng, Yihang Dai
arXiv (Cornell University)
Embedded Systems Design Techniques
preprint

Memory Admission and Result-Retention Sizing in an RV32IMC FPGA Soft Processor

Shuyu Xu, Yinfeng Zhou, Chenglei Peng, Yihang Dai
preprint en

Abstract

In FPGA soft processors with shared backing block RAM, memory admission can stall a multiplication after its product has been prepared. We study how admission timing changes result retention in PaceRV, our board-tested five-stage RV32IMC core, using fixed binaries and matched dynamic instructions. Offering data requests one edge earlier reduces geometric-mean Embench cycles by 18.2% and retention opportunities from 375,063 to 4,373. Admission and retention cycle gains are conditional and non-additive. Under slower reads, a 99% capture target selects an 18-edge validity window with registered requests and 28 edges with early requests, although no multiplication matched within both measurement intervals waits longer. A predeclared sizing rule predicts opportunity recovery exactly in all finite tests, including kernels excluded from model development. Independent timing analysis closely estimates execution-cycle savings. The results support choosing admission first, then deciding whether to extend validity and sizing it against an explicit capture or missed-opportunity objective.

arXiv (Cornell University)
Embedded Systems Design Techniques
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.

Laika access coming soon
Memory Admission and Result-Retention Sizing in an RV32IMC FPGA Soft Processor — Shuyu Xu, Yinfeng Zhou, et al. · arXiv (Cornell University) (2026) | TGRS Research Map | TGRS