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Found 7 papers, 0 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.
Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards
Prior work proposed to cache frequently accessed embeddings inside GPU memory as means to filter down the embedding layer traffic to CPU memory, but this paper observes several limitations with such cache design.
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training
Personalized recommendations are one of the most widely deployed machine learning (ML) workload serviced from cloud datacenters.
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.
NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units
To satisfy the compute and memory demands of deep neural networks, neural processing units (NPUs) are widely being utilized for accelerating deep learning algorithms.
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning
Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-intensive deep learning (DL) algorithm being deployed in today's datacenters.
Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep Learning
As the models and the datasets to train deep learning (DL) models scale, system architects are faced with new challenges, one of which is the memory capacity bottleneck, where the limited physical memory inside the accelerator device constrains the algorithm that can be studied.
