Optimizing RTL-Based FPGA Designs for ECU Performance Enhancement: Impact of Hierarchical Flattening Using Xilinx Vivado

A Performance, Low-Power, and Optimized FPGA Design for Real-Time Processing in Automotive Applications for Functional Safety of Electronic Control Units (ECUs) Project: The impact of hierarchical flattening on RTL designs synthesized through XILINX VIVADO for ECU optimization Structured design techniques provide advantages such as modularity, legibility, and reuse but can incur increased latency and resource overhead. Hierarchical flattening removes module bounds, allowing aggressive optimization at the cost of modularity. We analyzed three levels of flattening (None, Rebuilt, Full) to see their effects on synthesis time, area utilization, power efficiency and timing performance. The work includes hardware validation on FPGA board, Power and Timing analysis using Vivado tools such as report_power, report_timing_summary for hierarchical vs flattened designs. The results show that flattening decreases power consumption by up to 10% and synthesis time and area usage by 20% and 15%, respectively, therefore making the optimization strategy suitable for ECU. Aggressive flattening though can result in timing violations and makes debugging difficult. This is the best synthesis strategy presented here that gives the FPGA designers an option to balance performance and modularity, thereby allowing efficient ECU design.

Authors

Institutions

Publication Details

Published
2025-08-07
DOI
https://doi.org/10.1109/iccpct65132.2025.11176538
Primary Topic
Embedded Systems Design Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing RTL-Based FPGA Designs for ECU Performance Enhancement: Impact of Hierarchical Flattening Using Xilinx Vivado

Pulimi Mahesh, Suhaib Akthar, M. Parthiban, M Suresh Chinnapamthy et al.
Embedded Systems Design Techniques
article

Optimizing RTL-Based FPGA Designs for ECU Performance Enhancement: Impact of Hierarchical Flattening Using Xilinx Vivado

Pulimi Mahesh, Suhaib Akthar, M. Parthiban, M Suresh Chinnapamthy, Rr Rajarajavigraman, S Yeswanth
article en

Abstract

A Performance, Low-Power, and Optimized FPGA Design for Real-Time Processing in Automotive Applications for Functional Safety of Electronic Control Units (ECUs) Project: The impact of hierarchical flattening on RTL designs synthesized through XILINX VIVADO for ECU optimization Structured design techniques provide advantages such as modularity, legibility, and reuse but can incur increased latency and resource overhead. Hierarchical flattening removes module bounds, allowing aggressive optimization at the cost of modularity. We analyzed three levels of flattening (None, Rebuilt, Full) to see their effects on synthesis time, area utilization, power efficiency and timing performance. The work includes hardware validation on FPGA board, Power and Timing analysis using Vivado tools such as report_power, report_timing_summary for hierarchical vs flattened designs. The results show that flattening decreases power consumption by up to 10% and synthesis time and area usage by 20% and 15%, respectively, therefore making the optimization strategy suitable for ECU. Aggressive flattening though can result in timing violations and makes debugging difficult. This is the best synthesis strategy presented here that gives the FPGA designers an option to balance performance and modularity, thereby allowing efficient ECU design.

National Institute of Technology Meghalaya (IN)
Openalex Percentile: Top 69%
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.