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Found 20 papers, 3 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.
vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training
As large language models (LLMs) become widespread in various application domains, a critical challenge the AI community is facing is how to train these large AI models in a cost-effective manner.
Hera: A Heterogeneity-Aware Multi-Tenant Inference Server for Personalized Recommendations
While providing low latency is a fundamental requirement in deploying recommendation services, achieving high resource utility is also crucial in cost-effectively maintaining the datacenter.
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
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.
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.
SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures
Our work demonstrates that an ISP based large-scale GNN training system can achieve both high capacity storage and high performance, opening up opportunities for ML practitioners to train large GNN datasets without being hampered by the physical limitations of main memory size.
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.
PARIS and ELSA: An Elastic Scheduling Algorithm for Reconfigurable Multi-GPU Inference Servers
However, maximizing server utilization and system throughput is also crucial for ML service providers as it helps lower the total-cost-of-ownership.
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.
LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference
In cloud ML inference systems, batching is an essential technique to increase throughput which helps optimize total-cost-of-ownership.
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.
PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units
To amortize cost, cloud vendors providing DNN acceleration as a service to end-users employ consolidation and virtualization to share the underlying resources among multiple DNN service requests.
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.
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Further, we can enforce structured sparsity in the gate gradients to make the LSTM backward pass up to 45% faster than the state-of-the-art dense approach and 168% faster than the state-of-the-art sparsifying method on modern GPUs.
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning.
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory.
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design
The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU.
