Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 325

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories

Open access
Agrawal, Amogh, Jaiswal, Akhilesh, Lee, Chankyu, Roy, Kaushik·2018·IEEE Transactions on Circuits and Systems I: Regular Papers

Abstract

Silicon-based static random access memories (SRAM) and digital Boolean logic have been the workhorse of the state-of-the-art computing platforms. Despite tremendous strides in scaling the ubiquitous metal-oxide-semiconductor transistor, the underlying von-Neumann computing architecture has remained unchanged. The limited throughput and energy-efficiency of the state-of-the-art computing systems, to a large extent, result from the well-known von-Neumann bottleneck. The energy and throughput inefficiency of the von-Neumann machines have been accentuated in recent times due to the present emphasis on data-intensive applications such as artificial intelligence, machine learning, and cryptography. A possible approach towards mitigating the overhead associated with the von-Neumann bottleneck is to enable in-memory Boolean computations. In this paper, we present an augmented version of the conventional SRAM bit-cells, called the X-SRAM, with the ability to perform in-memory, vector Boolean computations, in addition to the usual memory storage operations. We propose at least six different schemes for enabling in-memory vector computations, including NAND, NOR, IMP (implication), XOR logic gates, with respect to different bit-cell topologies - the 8T cell and the 8+T Differential cell. In addition, we also present a novel `read-compute-store' scheme, wherein the computed Boolean function can be directly stored in the memory without the need of latching the data and carrying out a subsequent write operation. The feasibility of the proposed schemes has been verified using the predictive transistor models and detailed Monte-Carlo variation analysis. As an illustration, we also present the efficacy of the proposed in-memory computations by implementing advanced encryption standard algorithm on a non-standard von-Neumann machine wherein the conventional SRAM is replaced by X-SRAM. Our simulations indicated that up to 75% of memory accesses can be saved using the proposed techniques.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tcsi.2018.2848999

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 17 references

Cited by

See all 325

Metadata

Authors
Agrawal, A., Jaiswal, A., Lee, C., & Roy, K.
Venue
IEEE Transactions on Circuits and Systems I: Regular Papers
Published
2018-12
Volume
65
Issue
12
Pages
4219-4232
ISSN
  • 1549-8328 (ISSN-L)
  • 1558-0806
OpenAlex
W2775637085
Cited by
325
References
17
FWCI
8.56
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories. https://app.cochat.ai/papers/10.1109/tcsi.2018.2848999
The paper
Agrawal, A., Jaiswal, A., Lee, C., & Roy, K. (2018). X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories. IEEE Transactions on Circuits and Systems I: Regular Papers. https://doi.org/10.1109/tcsi.2018.2848999

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.