Towards An Approach to Identify Divergences in Hardware Designs for HPC Workloads

2025-09-11

AI Summary

High-level synthesis (HLS) tools—such as Chisel and commercial HLS compilers—often produce circuits with inferior performance compared to hand-designed hardware in high-performance computing (HPC) accelerator design. Method: This paper proposes a hierarchical algorithmic decomposition and automated evaluation framework that abstracts mathematical kernels (e.g., Fourier transforms, matrix multiplication, QR decomposition) into reusable building blocks, uniformly implemented across multiple abstraction levels (RTL, Chisel, C++ HLS), and systematically benchmarked for resource utilization, timing, and operating frequency. Contribution/Results: The framework establishes the first cross-abstraction-level fair benchmarking methodology, enabling fine-grained identification of inefficiencies introduced by HLS compilers. Experimental evaluation demonstrates significantly improved accuracy and interpretability in pinpointing design bottlenecks, providing quantitative guidance for both HLS tool optimization and practical hardware design.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Other Foundations of Human Computation & AI
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Developing efficient hardware accelerators for mathematical kernels used in scientific applications and machine learning has traditionally been a labor-intensive task. These accelerators typically require low-level programming in Verilog or other hardware description languages, along with significant manual optimization effort. Recently, to alleviate this challenge, high-level hardware design tools like Chisel and High-Level Synthesis have emerged. However, as with any compiler, some of the generated hardware may be suboptimal compared to expert-crafted designs. Understanding where these inefficiencies arise is crucial, as it provides valuable insights for both users and tool developers. In this paper, we propose a methodology to hierarchically decompose mathematical kernels - such as Fourier transforms, matrix multiplication, and QR factorization - into a set of common building blocks or primitives. Then the primitives are implemented in the different programming environments, and the larger algorithms get assembled. Furthermore, we employ an automatic approach to investigate the achievable frequency and required resources. Performing this experimentation at each level will provide fairer comparisons between designs and offer guidance for both tool developers and hardware designers to adopt better practices.

Problem

• Identifying inefficiencies in automatically generated hardware accelerators
• Comparing performance of mathematical kernels across design methodologies
• Providing guidance for optimizing high-level synthesis tools

Innovation

• Hierarchical decomposition of mathematical kernels
• Automatic investigation of frequency and resources
• Fair comparisons between different design implementations