Tsung-Yung Jonathan Chang

16.4 An 89TOPS/W and 16.3TOPS/mm² All-Digital SRAM-Based Full-Precision Compute-In Memory Macro in 22nm for Machine-Learning Edge Applications

Yu-Der Chih, Po-Hao Lee, Hidehiro Fujiwara et al. · 2021 · 308 citations

Artificial Intelligence, Engineering, Machine Learning +18

A 5-nm 254-TOPS/W 221-TOPS/mm² Fully-Digital Computing-in-Memory Macro Supporting Wide-Range Dynamic-Voltage-Frequency Scaling and Simultaneous MAC and Write Operations

Hidehiro Fujiwara, H. Mori, Wei-Chang Zhao et al. · 2022 IEEE International Solid- State Circuits Conference (ISSCC) · 2022 · 183 citations

Engineering, Vlsi Design, Emerging Memory Technology +18

16.1 A 22nm 4Mb 8b-Precision ReRAM Computing-in-Memory Macro with 11.91 to 195.7TOPS/W for Tiny AI Edge Devices

Cheng-Xin Xue, Je-Min Hung, Hui-Yao Kao et al. · 2021 · 174 citations

Engineering, Energy Efficiency, Emerging Memory Technology +21

An 8-Mb DC-Current-Free Binary-to-8b Precision ReRAM Nonvolatile Computing-in-Memory Macro using Time-Space-Readout with 1286.4-21.6TOPS/W for Edge-AI Devices

Je-Min Hung, Yen-Hsiang Huang, Sheng-Po Huang et al. · 2022 IEEE International Solid- State Circuits Conference (ISSCC) · 2022 · 112 citations

Non-volatile Memory, Engineering, Computer Architecture +19

A four-megabit compute-in-memory macro with eight-bit precision based on CMOS and resistive random-access memory for AI edge devices

Je-Min Hung, Cheng-Xin Xue, Hui-Yao Kao et al. · Nature Electronics · 2021 · 107 citations

Hardware Security, Four-megabit Compute-in-memory Macro, Engineering +13