16 citations · 25 across the 3 of their papers we have counts for
5 papers
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…
Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models
Adith Boloor, Xin He, Christopher Gill +2
Recent advances in machine learning, especially techniques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which ofte…
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…
Energy-dissipation Limits in Variance-based Computing
Sri Harsha Kondapalli, Xuan Zhang, Shantanu Chakrabartty
Variance-based logic (VBL) uses the fluctuations or the variance in the state of a particle or a physical quantity to represent different logic levels. In this letter we show that…