26 citations · 42 across the 6 of their papers we have counts for
6 papers
ThermoDSE: A Thermal-Aware and Comprehensive Design Space Exploration for Chiplet-Based DNN Accelerators
Jian Peng, Hanwei Fan, Jingbo Jiang +2
Chiplet-based DNN accelerators provide a scalable path to balance performance and yield for modern AI workloads. However, such systems face critical challenges in area and thermal…
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning
Xiaoyu Gan, Jingbo Jiang, Jingyang Zhu +3
Substantial efforts have been devoted to alleviating the impact of the long-tailed class distribution in federated learning. In this work, we observe an interesting phenomenon that…
Tight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementation
Xizi Chen, Jingyang Zhu, Jingbo Jiang +1
The unstructured sparsity after pruning poses a challenge to the efficient implementation of deep learning models in existing regular architectures like systolic arrays. On the oth…
Accelerating Large Kernel Convolutions with Nested Winograd Transformation.pdf
Jingbo Jiang, Xizi Chen, Chi-Ying Tsui
Recent literature has shown that convolutional neural networks (CNNs) with large kernels outperform vision transformers (ViTs) and CNNs with stacked small kernels in many computer…
CompRRAE: RRAM-based Convolutional Neural Network Accelerator with Reduced Computations through a Runtime Activation Estimation
Xizi Chen, Jingyang Zhu, Jingbo Jiang +1
Recently Resistive-RAM (RRAM) crossbar has been used in the design of the accelerator of convolutional neural networks (CNNs) to solve the memory wall issue. However, the intensive…
SparseNN: An Energy-Efficient Neural Network Accelerator Exploiting Input and Output Sparsity
Jingyang Zhu, Jingbo Jiang, Xizi Chen +1
Contemporary Deep Neural Network (DNN) contains millions of synaptic connections with tens to hundreds of layers. The large computation and memory requirements pose a challenge to…