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08/23/2023
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
Large language models (LLMs) based on transformers have made significant...
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02/23/2023
Hera: A Heterogeneity-Aware Multi-Tenant Inference Server for Personalized Recommendations
While providing low latency is a fundamental requirement in deploying re...
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01/26/2023
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
On-device machine learning (ML) inference can enable the use of private ...
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08/26/2022
DiVa: An Accelerator for Differentially Private Machine Learning
The widespread deployment of machine learning (ML) is raising serious co...
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05/10/2022
SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures
Graph neural networks (GNNs) can extract features by learning both the r...
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05/10/2022
Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards
Personalized recommendation models (RecSys) are one of the most popular ...
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05/02/2022
ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse
Homomorphic Encryption (HE) is one of the most promising post-quantum cr...
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03/01/2022
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 technol...
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02/27/2022
PARIS and ELSA: An Elastic Scheduling Algorithm for Reconfigurable Multi-GPU Inference Servers
In cloud machine learning (ML) inference systems, providing low latency ...
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12/31/2021
BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption
Homomorphic encryption (HE) enables the secure offloading of computation...
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10/25/2020
LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference
In cloud ML inference systems, batching is an essential technique to inc...
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10/25/2020
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training
Personalized recommendations are one of the most widely deployed machine...
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05/12/2020
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations
Personalized recommendations are the backbone machine learning (ML) algo...
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11/15/2019
NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units
To satisfy the compute and memory demands of deep neural networks, neura...
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09/06/2019
PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units
To amortize cost, cloud vendors providing DNN acceleration as a service ...
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08/08/2019
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...
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02/18/2019
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,...
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06/01/2018
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Exploiting sparsity enables hardware systems to run neural networks fast...
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05/23/2017
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have emerged as a fundamental techn...
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05/03/2017
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Popular deep learning frameworks require users to fine-tune their memory...
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02/25/2016