In-Memory Implementation of an Approximate Adder With Reduced Latency and Error

In-memory computing has been a prominent solution to Von Neumann bottleneck that degrades the performance of a computing system. Approximate computing is widely used to improve the performance of multimedia and other applications that are error-tolerant. Approximate adders being the basic units used to design other complex units, get benefited when implemented in-memory by taking the advantages of both in-memory computing and approximate computing. In this work, we have improved the speculative carry select adder to minimize error and critical path delay by eliminating multiplexers. The proposed adder achieves less critical path, area, improved error characteristics such as error rate, normalized mean error distance and mean relative error distance when compared to the state-of-the-art approximate adders. Error rate of the proposed adder is 34.48% less than the best reported 32-bit adder with sub-adder size of 8-bit. When the proposed approximate adders are implemented in-memory using majority logic, they achieve better performance compared to the existing in-memory approximate adders. Latency of the proposed adders is observed to be a constant irrespective of adder size for a fixed sub-adder size.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2024-12-18
DOI
https://doi.org/10.1109/tcsi.2024.3511955
Citations
1
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.49
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

In-Memory Implementation of an Approximate Adder With Reduced Latency and Error

John Reuben, Vikramkumar Pudi, Vijaya Lakshmi
1 citations
IEEE Transactions on Circuits and Systems I Regular Papers
Parallel Computing and Optimization Techniques
0.49
article

In-Memory Implementation of an Approximate Adder With Reduced Latency and Error

John Reuben, Vikramkumar Pudi, Vijaya Lakshmi
article en
1 citations

Abstract

In-memory computing has been a prominent solution to Von Neumann bottleneck that degrades the performance of a computing system. Approximate computing is widely used to improve the performance of multimedia and other applications that are error-tolerant. Approximate adders being the basic units used to design other complex units, get benefited when implemented in-memory by taking the advantages of both in-memory computing and approximate computing. In this work, we have improved the speculative carry select adder to minimize error and critical path delay by eliminating multiplexers. The proposed adder achieves less critical path, area, improved error characteristics such as error rate, normalized mean error distance and mean relative error distance when compared to the state-of-the-art approximate adders. Error rate of the proposed adder is 34.48% less than the best reported 32-bit adder with sub-adder size of 8-bit. When the proposed approximate adders are implemented in-memory using majority logic, they achieve better performance compared to the existing in-memory approximate adders. Latency of the proposed adders is observed to be a constant irrespective of adder size for a fixed sub-adder size.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 72(5)
Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Indian Institute of Technology Tirupati (IN)
Openalex Percentile: Top 33%
Parallel Computing and Optimization Techniques
0.49
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.