25 citations · 28 across the 3 of their papers we have counts for
7 papers
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…
SMART Paths for Latency Reduction in ReRAM Processing-In-Memory Architecture for CNN Inference
Sho Ko, Shimeng Yu
This research work proposes a design of an analog ReRAM-based PIM (processing-in-memory) architecture for fast and efficient CNN (convolutional neural network) inference. For the o…
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…
High-Throughput In-Memory Computing for Binary Deep Neural Networks with Monolithically Integrated RRAM and 90nm CMOS
Shihui Yin, Xiaoyu Sun, Shimeng Yu +1
Deep learning hardware designs have been bottlenecked by conventional memories such as SRAM due to density, leakage and parallel computing challenges. Resistive devices can address…
Harnessing Intrinsic Noise in Memristor Hopfield Neural Networks for Combinatorial Optimization
Fuxi Cai, Suhas Kumar, Thomas Van Vaerenbergh +8
We describe a hybrid analog-digital computing approach to solve important combinatorial optimization problems that leverages memristors (two-terminal nonvolatile memories). While p…