Year: 2024
Type: conference-paper
Abstract: While High-Level Synthesis (HLS) enables circuit generation directly from languages like C/C++ and OpenCL, optimal implementations require additional design specification through compiler directives. Automated optimization of these directives requires evaluation of candidate designs, but is bottlenecked by the high computational cost. Graph Neural Networks (GNNs) have recently emerged as the state-of-the-art for estimating the required Quality of Results (QoR) directly from high-level source code, but they come with associated challenges: the difficulty of information propagation, the complexity of software-oriented graph representations, and high levels of extraneous computation increase both error and computational cost. We present Balor, an HLS source code evaluator, which consists of a graph compiler and a GNN-based QoR estimator. The modular graph compiler is tailor-made for HLS QoR estimation, producing smaller graphs that contain only HLS-specific information. Balor analytically propagates important information across the graph, allowing us to pivot to small, local GNNs, while outperforming more expensive networks. Additionally, we make use of the natural hierarchy of an HLS kernel, and cluster the graph by basic block, to further propagate and process information---without the need for additional datasets. Combined, our contributions simultaneously reduce estimation error by 41%, and computational cost by 82%. Balor is open-sourced at github.com/emmet-murphy/balor
Authors: Emmet Murphy, Lana Josipović
Institution: ETH Zurich
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4409306160