27 citations · 101 across the 11 of their papers we have counts for
11 papers
SphereFed: Hyperspherical Federated Learning
Xin Dong, Sai Qian Zhang, Ang Li +1
Federated Learning aims at training a global model from multiple decentralized devices (i.e. clients) without exchanging their private local data. A key challenge is the handling o…
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding
Sai Qian Zhang, Bradley McDanel, H. T. Kung
Block Floating Point (BFP) can efficiently support quantization for Deep Neural Network (DNN) training by providing a wide dynamic range via a shared exponent across a group of val…
Succinct and Robust Multi-Agent Communication With Temporal Message Control
Sai Qian Zhang, Jieyu Lin, Qi Zhang
Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL). Howe…
Term Revealing: Furthering Quantization at Run Time on Quantized DNNs
H. T. Kung, Bradley McDanel, Sai Qian Zhang
We present a novel technique, called Term Revealing (TR), for furthering quantization at run time for improved performance of Deep Neural Networks (DNNs) already quantized with con…
On the Robustness of Cooperative Multi-Agent Reinforcement Learning
Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang +2
In cooperative multi-agent reinforcement learning (c-MARL), agents learn to cooperatively take actions as a team to maximize a total team reward. We analyze the robustness of c-MAR…
RTN: Reparameterized Ternary Network
Yuhang Li, Xin Dong, Sai Qian Zhang +3
To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed…
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Sai Qian Zhang, Qi Zhang, Jieyu Lin
Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications. However, achieving effic…
Full-stack Optimization for Accelerating CNNs with FPGA Validation
Bradley McDanel, Sai Qian Zhang, H. T. Kung +1
We present a full-stack optimization framework for accelerating inference of CNNs (Convolutional Neural Networks) and validate the approach with field-programmable gate arrays (FPG…
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization
H. T. Kung, Bradley McDanel, Sai Qian Zhang
This paper describes a novel approach of packing sparse convolutional neural networks for their efficient systolic array implementations. By combining subsets of columns in the ori…
InferBeam: A Fast Beam Alignment Protocol for Millimeter-wave Networking
Sai Qian Zhang, H. T. Kung, Youngjune Gwon
We introduce fast millimeter-wave base station (BS) and its antenna sector selection for user equipment based on its location. Using a conditional random field inference model with…
Partitioning Large Scale Deep Belief Networks Using Dropout
Yanping Huang, Sai Zhang
Deep learning methods have shown great promise in many practical applications, ranging from speech recognition, visual object recognition, to text processing. However, most of the…