CONV-SRAM: An Energy-Efficient SRAM With In-Memory Dot-Product Computation for Low-Power Convolutional Neural Networks-Reference-Cited by-同舟云学术

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CONV-SRAM: An Energy-Efficient SRAM With In-Memory Dot-Product Computation for Low-Power Convolutional Neural Networks

Published:2019-01 Issue:1 Volume:54 Page:217-230
ISSN:0018-9200
Container-title:IEEE Journal of Solid-State Circuits
language:
Short-container-title:IEEE J. Solid-State Circuits

Author:

Biswas Avishek^ORCID,Chandrakasan Anantha P.

Funder

Intel Corporation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/4/8613098/08579538.pdf?arnumber=8579538

Cited by 265 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. A 28-nm 9T SRAM-based CIM macro with capacitance weighting module and redundant array-assisted ADC;Microelectronics Journal;2024-11

2. Capacitance and Conductance Compensation Methods for Efficient Computing‐In‐Memory Designs;Advanced Electronic Materials;2024-09-06

3. PIMSAB: A P rocessing- I n- M emory System with S patially- A ware Communication and B it-Serial-Aware Computation;ACM Transactions on Architecture and Code Optimization;2024-09-05

4. Bi-Directional and Operand-Controllable In-Memory Computing for Boolean Logic and Search Operations with Row and Column Directional SRAM (RC-SRAM);Micromachines;2024-08-22

5. Coupled 7T 1C SRAM based in-memory computing architecture with gain/offset error auto-compensated SAR ADC;Proceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design;2024-08-05

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