26 citations · 58 across the 18 of their papers we have counts for
7 papers · 1 filter
A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A Single-ADC Interface
Guodong Yin, Mufeng Zhou, Yiming Chen +9
Performing data-intensive tasks in the von Neumann architecture is challenging to achieve both high performance and power efficiency due to the memory wall bottleneck. Computing-in…
Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics
Ruoyang Liu, Chenhan Wei, Yixiong Yang +3
Data quantization is an effective method to accelerate neural network training and reduce power consumption. However, it is challenging to perform low-bit quantized training: the c…
Ferroelectric FET-based strong physical unclonable function: a low-power, high-reliable and reconfigurable solution for Internet-of-Things security
Xinrui Guo, Xiaoyang Ma, Franz Muller +5
Hardware security has been a key concern in modern information technologies. Especially, as the number of Internet-of-Things (IoT) devices grows rapidly, to protect the device secu…
GRAPHIC: GatheR-And-Process in Highly parallel with In-SSD Compression Architecture in Very Large-Scale Graph
Yiming Chen, Guohao Dai, Mufeng Zhou +8
Graph convolutional network (GCN), an emerging algorithm for graph computing, has achieved promising performance in graphstructure tasks. To achieve acceleration for data-intensive…
ALL-MASK: A Reconfigurable Logic Locking Method for Multicore Architecture with Sequential-Instruction-Oriented Key
Jianfeng Wang, Zhonghao Chen, Jiahao Zhang +9
Intellectual property (IP) piracy has become a non-negligible problem as the integrated circuit (IC) production supply chain is becoming increasingly globalized and separated that…
YOLoC: DeploY Large-Scale Neural Network by ROM-based Computing-in-Memory using ResiduaL Branch on a Chip
Yiming Chen, Guodong Yin, Zhanhong Tan +6
Computing-in-memory (CiM) is a promising technique to achieve high energy efficiency in data-intensive matrix-vector multiplication (MVM) by relieving the memory bottleneck. Unfort…