Software-hardware co-design for fast and scalable training of deep learning recommendation models. - Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Zhihao Jia; Andrew Tulloch; Srinivas Sridharan 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; et al. (ISCA, 2022)

MTIA: First Generation Silicon Targeting Meta's Recommendation Systems. - Amin Firoozshahian; Joel Coburn; Roman Levenstein; Rakesh Nattoji; Ashwin Kamath; Olívia Wu; Gurdeepak Grewal; Harish Aepala; Bhasker Jakka; Bob Dreyer; Adam Hutchin; Utku Diril; et al. (ISCA, 2023)

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel. - Yanli Zhao; Andrew Gu; Rohan Varma; Liang Luo; Chien-Chin Huang; Min Xu; Less Wright; Hamid Shojanazeri; Myle Ott; Sam Shleifer; Alban Desmaison; Can Balioglu; et al. (Proc. VLDB Endow., 2023)

PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation. - Jason Ansel; Edward Z. Yang; Horace He; Natalia Gimelshein; Animesh Jain; Michael Voznesensky; Bin Bao; Peter Bell 008; David Berard; Evgeni Burovski; Geeta Chauhan; Anjali Chourdia; et al. (ASPLOS (2), 2024)

Meta's Second Generation AI Chip: Model-Chip Co-Design and Productionization Experiences. - Joel Coburn; Chunqiang Tang; Sameer Abu Asal; Neeraj Agrawal; Raviteja Chinta; Harish Dattatraya Dixit; Brian Dodds; Saritha Dwarakapuram; Amin Firoozshahian; Cao Gao; Kaustubh Gondkar; Tyler Graf; et al. (ISCA, 2025)