79 citations · 144 across the 6 of their papers we have counts for
6 papers
MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches
Xin Wang, Chi Ma, Shaobin Chen +14
Generative recommendation (GR) offers superior modeling capabilities but suffers from prohibitive inference costs due to the repeated encoding of long user histories. While cross-r…
HierarchicalKV: A GPU Hash Table with Cache Semantics for Continuous Online Embedding Storage
Haidong Rong, Jiashu Yao, Matthias Langer +12
Traditional GPU hash tables preserve every inserted key -- a dictionary assumption that wastes scarce High Bandwidth Memory (HBM) when embedding tables routinely exceed single-GPU…
A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models
Yingcan Wei, Matthias Langer, Fan Yu +4
Recommendation systems are of crucial importance for a variety of modern apps and web services, such as news feeds, social networks, e-commerce, search, etc. To achieve peak predic…
Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference
Joey Wang, Yingcan Wei, Minseok Lee +9
In this talk, we introduce Merlin HugeCTR. Merlin HugeCTR is an open source, GPU-accelerated integration framework for click-through rate estimation. It optimizes both training and…
TDACNN: Target-domain-free Domain Adaptation Convolutional Neural Network for Drift Compensation in Gas Sensors
Yuelin Zhang, Sihao Xiang, Zehuan Wang +4
Sensor drift is a long-existing unpredictable problem that deteriorates the performance of gaseous substance recognition, calling for an antidrift domain adaptation algorithm. Howe…
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity
Cong Guo, Bo Yang Hsueh, Jingwen Leng +7
Network pruning can reduce the high computation cost of deep neural network (DNN) models. However, to maintain their accuracies, sparse models often carry randomly-distributed weig…