Quantifying the Effect of HCLs on a Fixed-Microarchitecture MXFP4 Accelerator

2026-09-16

AI Summary

本文通过固定设计比较了多种硬件构造语言（HCLs）在MXFP4加速器上的效果，旨在区分语言影响与设计影响。

Abstract

Hardware Construction Languages (HCLs) aim to improve hardware design productivity while generating register-transfer-level (RTL) circuits without changing the designer's microarchitecture. However, most comparisons between HCLs are either qualitative or evaluate quality of results (QoR) across different designs, making it difficult to separate language effects from design effects. This paper compares the most widely used HCLs using the same fixed design, the OCP MXFP4 block dot product, a quantization primitive at the heart of edge Physical-AI inference, implemented as a single 12-stage, II=1 pipeline. A SystemVerilog baseline is followed by implementations in Chisel, SpinalHDL, Amaranth, Clash, Bluespec, and C++ for high-level synthesis (HLS). Every variant goes through the same flow on the same Artix-7 device set at 100 MhZ, driven by a RISC-V soft core. With the micro-architecture held constant, the comparison is clean: every variant meets timing, and the HCLs match or even undercut hand-written RTL in area. The remaining differences stem not from the algorithm but from how each back end lowers arithmetic, and from a single width choice that silently toggles DSP inference. Unlike HLS, where design decisions are limited to pragmas, the HCLs achieve comparable area and timing. Therefore, the choice comes down to ecosystem fit and interface needs rather than QoR.

Problem

• Hardware Construction Languages
• Fixed-Microarchitecture
• Quantization Primitive
• Quality of Results
• Register-Transfer-Level

Innovation

• Hardware Construction Languages
• Fixed-Microarchitecture
• Register-Transfer-Level
• Quality of Results
• Arithmetic Lowering