Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

HLScope: High-Level Performance Debugging for FPGA Designs

Authors: Young-Kyu Choi; Jason Cong;

HLScope: High-Level Performance Debugging for FPGA Designs

Abstract

In their quest for further optimization, field-programmable gate array (FPGA) designers often spend considerable time trying to identify the performance bottleneck in a current design. But since FPGAs do not have built-in high-level probes for performance analysis, manual effort is required to insert custom hardware monitors. This, however, is a time-consuming process which calls for automation. Previous work automates the process of inserting hardware monitors into the communication channels or the finite-state machine, but the instrumentation is applied in low-level hardware description languages (HDL) which limits the comprehensibility in identifying the root cause of stalls. Instead, we propose a performance debugging methodology based on high-level synthesis (HLS). High-level analysis allows tracing the cause of stalls on a function or loop level, which provides a more intuitive feedback that can be used to pinpoint the performance bottleneck. In this paper we propose HLScope, a source-to-source transformation framework based on Vivado HLS for automated performance analysis. We present a method for analyzing the information collected from the software simulation to estimate the stall rate and its cause without the need for FPGA bitstream generation. For detailed analysis, an in-FPGA analysis method is proposed that can be natively integrated into the HLS environment. Experiments show that the parameter extraction from the simulation process is orders of magnitude faster than bitstream generation, with a 2.2% cycle difference on average. In-FPGA flow consumes only about 170 LUTs and a BRAM per monitored module and provides cycle-accurate results.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    23
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Top 10%
Top 10%
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!