Huang, Sheng-Po
National Tsing Hua University
5 papers on CoChat
Most cited papers
- Verified
15.4 A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices
Xue, C.-X., Huang, T.-Y., Liu, J.-S., Chang, T.-W., Kao, H.-Y., Wang, J.-H., Liu, T.-W., Wei, S.-Y., Huang, S.-P., Wei, W.-C., et al. (2020) · 2020 IEEE International Solid- State Circuits Conference - (ISSCC)
Advanced Memory and Neural ComputingLow-power high-performance VLSI design
Cited by 230
- Verified
16.1 A 22nm 4Mb 8b-Precision ReRAM Computing-in-Memory Macro with 11.91 to 195.7TOPS/W for Tiny AI Edge Devices
Xue, C.-X., Hung, J.-M., Kao, H.-Y., Huang, Y.-H., Huang, S.-P., Chang, F.-C., Chen, P., Liu, T.-W., Jhang, C.-J., Su, C.-I., et al. (2021) · 2021 IEEE International Solid- State Circuits Conference (ISSCC)
Advanced Memory and Neural ComputingLow-power high-performance VLSI design
Cited by 182
- Verified
A CMOS-integrated compute-in-memory macro based on resistive random-access memory for AI edge devices
Xue, C.-X., Chiu, Y.-C., Liu, T.-W., Huang, T.-Y., Liu, J.-S., Chang, T.-W., Kao, H.-Y., Wang, J.-H., Wei, S.-Y., Lee, C.-Y., et al. (2020) · Nature Electronics
Advanced Memory and Neural ComputingLow-power high-performance VLSI design
Cited by 181
- Verified
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
Hung, J.-M., Huang, Y.-H., Huang, S.-P., Chang, F.-C., Wen, T.-H., Su, C.-I., Khwa, W.-S., Lo, C.-C., Liu, R.-S., Hsieh, C.-C., et al. (2022) · 2022 IEEE International Solid- State Circuits Conference (ISSCC)
Advanced Memory and Neural ComputingLow-power high-performance VLSI design
Cited by 125
- Verified
A 40-nm, 2M-Cell, 8b-Precision, Hybrid SLC-MLC PCM Computing-in-Memory Macro with 20.5 - 65.0TOPS/W for Tiny-Al Edge Devices
Khwa, W.-S., Chiu, Y.-C., Jhang, C.-J., Huang, S.-P., Lee, C.-Y., Wen, T.-H., Chang, F.-C., Yu, S.-M., Lee, T.-Y., & Chang, M.-F. (2022) · 2022 IEEE International Solid- State Circuits Conference (ISSCC)
Advanced Memory and Neural ComputingLow-power high-performance VLSI design
Cited by 125
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