Match-Line Division and Control to Reduce Power Dissipation in Content Addressable Memory

Hussain, Sheikh Wasmir; Mahendra, Telajala Venkata; Mishra, Sandeep; Dandapat, Anup

doi:10.1109/tce.2018.2859623

Cited by 24 publications

(16 citation statements)

References 21 publications

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“…Segmented ML technique proved to be the best to handle all the performance metrics. Table 2 Performance comparison summary of ML-sensing techniques Precharge high [6,14,15] Current-race scheme [22,33] Precharge free [29][30][31][32] Low swing [11,[34][35][36] Segmented [37][38][39][40][41][42][43][44][45] Sele. precharge [46][47][48]…”

Section: Resultsmentioning

confidence: 99%

“…(iv) Low-swing scheme [11,[34][35][36]: low-swing ML approaches to reduce dynamic power. (v) Segmented ML evaluation [37][38][39][40][41][42][43][44][45]: a non-uniform distribution of ML evaluation results by segmenting ML and designing sections with different lengths and structures to improve the power-delay trade-off. (vi) Selective pre-charge sensing scheme [46][47][48]: another nonuniform power distribution among the MLs based on the higher mismatch probability in initial sections.…”

Section: Sensing Techniquesmentioning

confidence: 99%

“…This is because of the following factors: (i) all the mismatching MLs discharge from pre-charged (V DD ) to GND as the MLs are evaluated during the search (all the mismatching MLs change their states from pre-charged as the MLs are evaluated during search). (ii) For a mismatching ML, the number of discharge paths is in linear proportion to the number of mismatching cells [37][38][39][40][41][42][43][44][45]. At most all but one MLs mismatch for a search and hence the MLs are required to be re-pre-charged prior to next search.…”

Section: Segmented ML Evaluationmentioning

confidence: 99%

“…Unlike the conventional non-segmented ML sensing, the segmented architecture shown in Fig. 15 [42] performs sensing according to the selective evaluation of Section 2 based on the evaluation result of Section 1. Signals Di, and Di are the storage nodes provided by corresponding 6T-SRAM cells.…”

Section: Segmented ML Evaluationmentioning

confidence: 99%

“…where N and K represent the number of cells covering the length of each section of a two segment ML. It is valid for MLs with two segments [42,44] as in many of the CAM works on segmentation.…”

Section: Performance Comparison Of ML Sensing Techniquesmentioning

confidence: 99%

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The analogy of matchline sensing techniques for content addressable memory (CAM)

Mishra¹,

Mahendra

Hussain

et al. 2020

IET Computers & Digital Techniques

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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.

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