11 citations · 27 across the 5 of their papers we have counts for
5 papers
Voxel-CIM: An Efficient Compute-in-Memory Accelerator for Voxel-based Point Cloud Neural Networks
Xipeng Lin, Shanshi Huang, Hongwu Jiang
The 3D point cloud perception has emerged as a fundamental role for a wide range of applications. In particular, with the rapid development of neural networks, the voxel-based netw…
MICSim: A Modular Simulator for Mixed-signal Compute-in-Memory based AI Accelerator
Cong Wang, Zeming Chen, Shanshi Huang
This work introduces MICSim, an open-source, pre-circuit simulator designed for early-stage evaluation of chip-level software performance and hardware overhead of mixed-signal comp…
Mitigating Adversarial Attack for Compute-in-Memory Accelerator Utilizing On-chip Finetune
Shanshi Huang, Hongwu Jiang, Shimeng Yu
Compute-in-memory (CIM) has been proposed to accelerate the convolution neural network (CNN) computation by implementing parallel multiply and accumulation in analog domain. Howeve…
New Security Challenges on Machine Learning Inference Engine: Chip Cloning and Model Reverse Engineering
Shanshi Huang, Xiaochen Peng, Hongwu Jiang +2
Machine learning inference engine is of great interest to smart edge computing. Compute-in-memory (CIM) architecture has shown significant improvements in throughput and energy eff…
DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip Training
Xiaochen Peng, Shanshi Huang, Hongwu Jiang +2
DNN+NeuroSim is an integrated framework to benchmark compute-in-memory (CIM) accelerators for deep neural networks, with hierarchical design options from device-level, to circuit-l…