Resistive Bloom Filters: From Approximate Membership to Approximate Computing with Bounded Errors

Akhlaghi, Vahideh; Rahimi, Abbas; Gupta, Rajesh K.

doi:10.3850/9783981537079_0530

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…There is a significant amount of redundant data when processing streaming applications [1], [2]. Associative memory was introduced to exploit this observation and decrease the number of redundant computations [3], [4], [5], [6], [7], [8], [9]. In hardware, associative memories are implemented as look up tables using ternary content addressable memories (TCAMs) [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Approximate Computing Using Multiple-Access Single-Charge Associative Memory

Imani

Patil

Rosing

2018

IEEE Trans. Emerg. Topics Comput.

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Memory-based computing using associative memory is a promising way to reduce the energy consumption of important classes of streaming applications by avoiding redundant computations. A set of frequent patterns that represent basic functions are pre-stored in Ternary Content Addressable Memory (TCAM) and reused. The primary limitation to using associative memory in modern parallel processors is the large search energy required by TCAMs. In TCAMs, all rows that match, except hit rows, precharge and discharge for every search operation, resulting in high energy consumption. In this paper, we propose a new Multiple-Access Single-Charge (MASC) TCAM architecture which is capable of searching TCAM contents multiple times with only a single precharge cycle. In contrast to previous designs, the MASC TCAM keeps the match-line voltage of all miss-rows high and uses their charge for the next search operation, while only the hit rows discharge. We use periodic refresh to control the accuracy of the search. We also implement a new type of approximate associative memory by setting longer refresh times for MASC TCAMs, which yields search results within 1-2 bit Hamming distances of the exact value. To further decrease the energy consumption of MASC TCAM and reduce the area, we implement MASC with crossbar TCAMs. Our evaluation on AMD Southern Island GPU shows that using MASC (crossbar MASC) associative memory can improve the average floating point units energy efficiency by 33.4%, 38.1%, and 36.7% (37.7%, 42.6%, and 43.1%) for exact matching, selective 1-HD and 2-HD approximations respectively, providing an acceptable quality of service (PSNR>30dB and average relative error<10%). This shows that MASC (crossbar MASC) can achieve 1.77X (1.93X) higher energy savings as compared to the state of the art implementation of GPGPU that uses voltage overscaling on TCAM.

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Section: Introductionmentioning

confidence: 99%

Approximate Computing Using Multiple-Access Single-Charge Associative Memory

Imani

Patil

Rosing

2018

IEEE Trans. Emerg. Topics Comput.

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“…The trade-off made by ABF for this flexibility is a slight reduction of the true positive rate (which is always 1 in CBF). It is important to note that a less than perfect true positive rate can be tolerated in many applications including networking [2], and generally in the area of approximate computing where errors and approximations are acceptable as long as the outcomes have a well-defined statistical behavior [3]. To the best of our knowledge, ABF is a novel simple construction of BFs, which makes them particularly useful in scenarios where a reduced true positive rate can be tolerated and where the number of the stored elements is unknown or changes dynamically with time.…”

Section: Introductionmentioning

confidence: 99%

Autoscaling Bloom filter: controlling trade-off between true and false positives

Kleyko

Rahimi

Gayler³

et al. 2019

Neural Comput & Applic

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A Bloom filter is a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called "autoscaling Bloom filters", which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of the performance as well as give a procedure for minimization of the false positive rate.

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