A Charge-Domain Scalable-Weight In-Memory Computing Macro With Dual-SRAM Architecture for Precision-Scalable DNN Accelerators

Author:

Lee EunyoungORCID,Han Taeyoung,Seo DongukORCID,Shin GicheolORCID,Kim JaerokORCID,Kim Seonho,Jeong Soyoun,Rhe Johnny,Park Jaehyun,Ko Jong HwanORCID,Lee YoonmyungORCID

Funder

National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT

Samsung Electronics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Cited by 45 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An XOR-10T SRAM computing-in-memory macro with current MAC operations and time-to-digital conversion for BNN edge processors;AEU - International Journal of Electronics and Communications;2024-07

2. Reconfigurable Precision SRAM-based Analog In-memory-compute Macro Design;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

3. SRAM-Based Hybrid Analog Compute-In-memory Architecture to Enhance the Signal Margin;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

4. An eDRAM-Based Computing-in-Memory Macro With Full-Valid-Storage and Channel-Wise-Parallelism for Depthwise Neural Network;IEEE Transactions on Circuits and Systems II: Express Briefs;2024-05

5. ConvMapSim: Modeling and Simulating Convolutional Weight Mapping for PIM Arrays;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22

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