A Prediction Packetizing Scheme for Reducing Channel Traffic in Transaction-Level Hardware/Software Co-Emulation

TL;DR

This paper introduces a predictive packetizing scheme that significantly reduces channel traffic in transaction-level hardware/software co-emulation, leading to substantial performance improvements.

Contribution

It proposes a novel prediction-based packetizing method to merge transactions into bursts, minimizing startup overhead and enhancing simulation efficiency.

Findings

Achieves up to 1500% performance gain under ideal prediction accuracy.

Reduces channel traffic by merging multiple transactions into bursts.

Demonstrates effectiveness of prediction and rollback in simulation acceleration.

Abstract

This paper presents a scheme for efficient channel usage between simulator and accelerator where the accelerator models some RTL sub-blocks in the accelerator-based hardware/software co-simulation while the simulator runs transaction-level model of the remaining part of the whole chip being verified. With conventional simulation accelerator, evaluations of simulator and accelerator alternate at every valid simulation time, which results in poor simulation performance due to startup overhead of simulator-accelerator channel access. The startup overhead can be reduced by merging multiple transactions on the channel into a single burst traffic. We propose a predictive packetizing scheme for reducing channel traffic by merging as many transactions into a burst traffic as possible based on 'prediction and rollback.' Under ideal condition with 100% prediction accuracy, the proposed method shows a performance gain of 1500% compared to the conventional one.