21 citations · 46 across the 8 of their papers we have counts for
8 papers
Memory-aware Scheduling for Complex Wired Networks with Iterative Graph Optimization
Shuzhang Zhong, Meng Li, Yun Liang +2
Memory-aware network scheduling is becoming increasingly important for deep neural network (DNN) inference on resource-constrained devices. However, due to the complex cell-level a…
OpenPARF: An Open-Source Placement and Routing Framework for Large-Scale Heterogeneous FPGAs with Deep Learning Toolkit
Jing Mai, Jiarui Wang, Zhixiong Di +3
This paper proposes OpenPARF, an open-source placement and routing framework for large-scale FPGA designs. OpenPARF is implemented with the deep learning toolkit PyTorch and suppor…
Systolic Computing on GPUs for Productive Performance
Hongbo Rong, Xiaochen Hao, Yun Liang +3
We propose a language and compiler to productively build high-performance {\it software systolic arrays} that run on GPUs. Based on a rigorous mathematical foundation (uniform recu…
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs
Caiwen Ding, Shuo Wang, Ning Liu +3
Deep neural networks (DNNs), as the basis of object detection, will play a key role in the development of future autonomous systems with full autonomy. The autonomous systems have…
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
Zhe Li, Shuo Wang, Caiwen Ding +3
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The recent pruning…
C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
Shuo Wang, Zhe Li, Caiwen Ding +4
Recently, significant accuracy improvement has been achieved for acoustic recognition systems by increasing the model size of Long Short-Term Memory (LSTM) networks. Unfortunately,…
CuLDA_CGS: Solving Large-scale LDA Problems on GPUs
Xiaolong Xie, Yun Liang, Xiuhong Li +1
Latent Dirichlet Allocation(LDA) is a popular topic model. Given the fact that the input corpus of LDA algorithms consists of millions to billions of tokens, the LDA training proce…
Optimizing the MapReduce Framework on Intel Xeon Phi Coprocessor
Mian Lu, Lei Zhang, Huynh Phung Huynh +5
With the ease-of-programming, flexibility and yet efficiency, MapReduce has become one of the most popular frameworks for building big-data applications. MapReduce was originally d…