394 citations · 536 across the 9 of their papers we have counts for
15 papers
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
Buyun Zhang, Liang Luo, Xi Liu +14
Learning feature interactions is important to the model performance of online advertising services. As a result, extensive efforts have been devoted to designing effective architec…
First-Generation Inference Accelerator Deployment at Facebook
Michael Anderson, Benny Chen, Stephen Chen +112
In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesse…
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale
Zhaoxia, Deng, Jongsoo Park +17
Tremendous success of machine learning (ML) and the unabated growth in ML model complexity motivated many ML-specific designs in both CPU and accelerator architectures to speed up…
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Xiaocong Du, Bhargav Bhushanam, Jiecao Yu +7
Deep learning recommendation systems at scale have provided remarkable gains through increasing model capacity (i.e. wider and deeper neural networks), but it comes at significant…
Efficient Soft-Error Detection for Low-precision Deep Learning Recommendation Models
Sihuan Li, Jianyu Huang, Ping Tak Peter Tang +4
Soft error, namely silent corruption of signal or datum in a computer system, cannot be caverlierly ignored as compute and communication density grow exponentially. Soft error dete…
FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference
Daya Khudia, Jianyu Huang, Protonu Basu +4
Deep learning models typically use single-precision (FP32) floating point data types for representing activations and weights, but a slew of recent research work has shown that com…