34 citations · 58 across the 7 of their papers we have counts for
7 papers
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
Haiyang Huang, Newsha Ardalani, Anna Sun +6
Mixture-of-Experts (MoE) models have gained popularity in achieving state-of-the-art performance in a wide range of tasks in computer vision and natural language processing. They e…
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
Hanieh Hashemi, Wenjie Xiong, Liu Ke +4
Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest o…
DisaggRec: Architecting Disaggregated Systems for Large-Scale Personalized Recommendation
Liu Ke, Xuan Zhang, Benjamin Lee +2
Deep learning-based personalized recommendation systems are widely used for online user-facing services in production datacenters, where a large amount of hardware resources are pr…
Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation
Liu Ke, Udit Gupta, Mark Hempstead +3
Personalized recommendation is an important class of deep-learning applications that powers a large collection of internet services and consumes a considerable amount of datacenter…
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Liu Ke, Udit Gupta, Carole-Jean Wu +18
Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embed…
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices
Weidong Cao, Liu Ke, Ayan Chakrabarti +1
Recent works propose neural network- (NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications. These NNADCs often r…
AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference
Xin He, Liu Ke, Wenyan Lu +2
The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate co…