Reconfigurable 10T SRAM for Energy-Efficient CAM Operation and In-Memory Computing

The limitations of the von Neumann architecture in terms of power consumption and throughput are increasingly evident. In-memory computing is a promising computing paradigm to alleviate this limitation. This article proposes a high-speed and low-power 10T compute-static random-access memory (CSRAM) capable of conducting rowwise search operations and executing in-memory logic functions efficiently. A self-suppressed discharge scheme is implemented to curtail the power consumption of the search operation by reducing the discharge swing of the match lines (MLs). The rowwise search scheme avoids vertical data storage, enhancing the compatibility between different operation modes. The proposed 10T SRAM architecture addresses the issue of sneak currents effectively when multiple lines are activated. Additionally, decoupled read ports eliminate compute access disturbance. To validate the design, a 4Kb array is designed with a 40-nm CMOS technology. At a supply voltage (VDD) of 1.1 V, the in-memory logic operations are capable of operating at a frequency of 752 MHz, consuming 29.2 fJ/bit. In binary content-addressable memory (BCAM) search mode, the minimum energy consumption of 0.51 fJ/bit occurs at 0.8 V and 120 MHz.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Published
2025-01-27
DOI
https://doi.org/10.1109/tvlsi.2025.3526973
Citations
9
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
20.43

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reconfigurable 10T SRAM for Energy-Efficient CAM Operation and In-Memory Computing

Jiedong Wang, Guangjun Xie, Zhihao Chen, Gang Liu et al.
9 citations
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Parallel Computing and Optimization Techniques
20.43
article

Reconfigurable 10T SRAM for Energy-Efficient CAM Operation and In-Memory Computing

Jiedong Wang, Guangjun Xie, Zhihao Chen, Gang Liu, Zhang Zhang
article en
9 citations

Abstract

The limitations of the von Neumann architecture in terms of power consumption and throughput are increasingly evident. In-memory computing is a promising computing paradigm to alleviate this limitation. This article proposes a high-speed and low-power 10T compute-static random-access memory (CSRAM) capable of conducting rowwise search operations and executing in-memory logic functions efficiently. A self-suppressed discharge scheme is implemented to curtail the power consumption of the search operation by reducing the discharge swing of the match lines (MLs). The rowwise search scheme avoids vertical data storage, enhancing the compatibility between different operation modes. The proposed 10T SRAM architecture addresses the issue of sneak currents effectively when multiple lines are activated. Additionally, decoupled read ports eliminate compute access disturbance. To validate the design, a 4Kb array is designed with a 40-nm CMOS technology. At a supply voltage (VDD) of 1.1 V, the in-memory logic operations are capable of operating at a frequency of 752 MHz, consuming 29.2 fJ/bit. In binary content-addressable memory (BCAM) search mode, the minimum energy consumption of 0.51 fJ/bit occurs at 0.8 V and 120 MHz.

IEEE Transactions on Very Large Scale Integration (VLSI) SystemsVol. 33(4)
Hefei University of Technology (CN), Shanghai Jiao Tong University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Anhui Province
Affordable and clean energy
Openalex Percentile: Top 1%
Parallel Computing and Optimization Techniques
20.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.