Open AccessThe analogy of matchline sensing techniques for content addressable memory (CAM)
Author(s) -
Mishra Sandeep,
Mahendra Telajala Venkata,
Hussain Sheikh Wasmir,
Dandapat Anup
Publication year - 2020
Publication title -
iet computers and digital techniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.219
H-Index - 46
eISSN - 1751-861X
pISSN - 1751-8601
DOI - 10.1049/iet-cdt.2019.0178
Subject(s) - analogy , computer science , content (measure theory) , content addressable memory , computer hardware , embedded system , computer architecture , artificial intelligence , mathematics , artificial neural network , mathematical analysis , philosophy , linguistics
Performance of a memory depends on the storage stability, yield and sensing speed. Differential input and the latching time of sense amplifiers are considered as primary performance factors in static random access memory. In a content addressable memory (CAM), the sensing is carried out through the matchline (ML) and the time for evaluation is the key to decide the search speed. The density of CAM is on a rise to accommodate a higher amount of information which increases the power dissipation associated with it. Issues such as the logical threshold variation and lower noise margin between match and mismatch are critical in the operation of a CAM. A good ML sensing technique can reduce the ML power with enhanced evaluation speed. This work provides an analogy of various ML sensing techniques based on their pre‐charging, evaluation and performance improvement strategies. Estimation on the power dissipation and evaluation time are made and in‐depth analysis on their power‐speed‐overhead trade‐off are carried on 64‐bit CAM macros.