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Found 5 papers, 1 papers with code
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
Differential privacy (DP) is widely being employed in the industry as a practical standard for privacy protection.
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
To tackle the high compute requirements of LLMs, the Mixture-of-Experts (MoE) architecture was introduced which is able to scale its model size without proportionally scaling up its computational requirements.
DiVa: An Accelerator for Differentially Private Machine Learning
The widespread deployment of machine learning (ML) is raising serious concerns on protecting the privacy of users who contributed to the collection of training data.
GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational.
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations
Personalized recommendations are the backbone machine learning (ML) algorithm that powers several important application domains (e. g., ads, e-commerce, etc) serviced from cloud datacenters.
