34 citations · 96 across the 7 of their papers we have counts for
8 papers
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
Zehao Dong, Weidong Cao, Muhan Zhang +3
The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs amo…
PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network
Huifeng Zhu, Haoqi Shan, Dean Sullivan +3
The ubiquitous presence of printed circuit boards (PCBs) in modern electronic systems and embedded devices makes their integrity a top security concern. To take advantage of the ec…
RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks with Fine-Grain Utilization
An Zou, Jing Li, Christopher D. Gill +1
Many emerging cyber-physical systems, such as autonomous vehicles and robots, rely heavily on artificial intelligence and machine learning algorithms to perform important system op…
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…