activity
20172024
most citedTight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementation

26 citations · 42 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2024

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…

cs.AR2023

A 137.5 TOPS/W SRAM Compute-in-Memory Macro with 9-b Memory Cell-Embedded ADCs and Signal Margin Enhancement Techniques for AI Edge Applications

Xiaomeng Wang, Fengshi Tian, Xizi Chen +7

In this paper, we propose a high-precision SRAM-based CIM macro that can perform 4x4-bit MAC operations and yield 9-bit signed output. The inherent discharge branches of SRAM cells…

cs.LG2021★ 26 cited

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…

cs.CV2021★ 2 cited

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…

eess.SP2019★ 11 cited

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…

cs.LG2017★ 3 cited

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…