Automated Hardware-Model Co-Design for Throughput-Accuracy Trade-offs in GNN-Based Recommenders

This report synthesises findings from 4 peer-reviewed papers addressing the following research question: What is the impact of joint hardware-model co-design automation on the throughput and accuracy trade-offs of deep learning-based recommender systems. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the impact of joint hardware-model co-design automation on the throughput and accuracy trade-offs of deep learning-based recommender systems?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.