Accelerating HLS Autotuning of Large, Highly-Parameterized Reconfigurable SoC Mappings / Johannes F. H Giesen.

Format:

Book

Thesis/Dissertation

Author/Creator:

Giesen, Johannes F. H., author.

Contributor:

University of Pennsylvania. Electrical and Systems Engineering, degree granting institution.

Language:

English

Subjects (All):

Electrical engineering.

Computer engineering.

Electrical and Systems Engineering--Penn dissertations.

Penn dissertations--Electrical and Systems Engineering.

Local Subjects:

Electrical engineering.

Computer engineering.

Electrical and Systems Engineering--Penn dissertations.

Penn dissertations--Electrical and Systems Engineering.

Physical Description:

1 online resource (136 pages)

Distribution:

Ann Arbor : ProQuest Dissertations & Theses, 2023

Contained In:

Dissertations Abstracts International 84-12B.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2022.

Language Note:

English

Summary:

High-level synthesis has accelerated the adoption of autotuners to explore the design spaces of applications mapped on systems-on-chip with reconfigurable logic. Design-space size increases exponentially in the number of design parameters, and building a single configuration of a full application easily consumes hours, so existing autotuners are frequently demonstrated with small kernels and small design spaces to render the problem tractable. This dissertation shows that 30+-parameter applications mapped on 200k+-LUT reconfigurable SoCs can be explored in less than 12 build times on an 8-core host using the model-based approach we refine. We explore various techniques to reduce the tuning time. At the heart of our tuner is an iterative refinement approach that builds a prediction model representing the design space. Our models are multi-fidelity models, which enable discontinuation of unpromising builds in multi-stage CAD flows. We organized the build resources into a pipeline to improve the tuning performance and increase the utilization of build resources. Build failures are mitigated in several ways. Invalid accelerator configurations are replaced with valid ones on-the-fly. Routing errors caused by congestion are mitigated through congestion models. Because the curse of dimensionality deteriorates the performance quickly as the number of parameters increases, we apply dimensionality reduction to focus on the most important parameters. To validate our approach, we injected 32-46 parameters, varying from pragmas to CAD tool parameters, into the Rosetta benchmarks. Compared to OpenTuner, our tuner succeeds 71% more often at finding mappings onto the ZCU102 within 12 hours, and the found mapping is 3.5x faster. Alternatively, we observed that tuning runs are on average at least 8.8x shorter.

Notes:

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Advisors: DeHon, Andre; Committee members: Smith, Jonathan; Mangharam, Rahul; Neuendorffer, Stephen.

Department: Electrical and Systems Engineering.

Ph.D. University of Pennsylvania 2023.

Local Notes:

School code: 0175

ISBN:

9798379751111

Access Restriction:

Restricted for use by site license.