1 citations · 1 across the 4 of their papers we have counts for
4 papers
DPUV4E: High-Throughput DPU Architecture Design for CNN on Versal ACAP
Guoyu Li, Pengbo Zheng, Jian Weng +1
Convolutional Neural Networks (CNNs) remain prevalent in computer vision applications, and FPGAs, known for their flexibility and energy efficiency, have become essential component…
LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator
Guoyu Li, Shengyu Ye, Chunyun Chen +6
The emergence of neural network capabilities invariably leads to a significant surge in computational demands due to expanding model sizes and increased computational complexity. T…
SigDLA: A Deep Learning Accelerator Extension for Signal Processing
Fangfa Fu, Wenyu Zhang, Zesong Jiang +8
Deep learning and signal processing are closely correlated in many IoT scenarios such as anomaly detection to empower intelligence of things. Many IoT processors utilize digital si…
DeepBurning-MixQ: An Open Source Mixed-Precision Neural Network Accelerator Design Framework for FPGAs
Erjing Luo, Haitong Huang, Cheng Liu +5
Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for a deep learning task promise significant advantages of both inference accuracy and computi…