Data Identification using L-CBF

counting bloom filters (CBFs) are used to improve upon the energy, delay, and complexity of various processor structures. CBFs improve the energy and speed of membership tests by maintaining an imprecise and compact representation of a large set to be searched. This studies the energy, delay, and area characteristics of two implementations for CBFs using full custom layouts in a commercial 0.13m fabrication technology. One implementation, S-CBF, uses an SRAM array of counts and a shared up/down counter. Our proposed implementation, L-CBF, utilizes an array of up/down linear feedback shift registers and local zero detectors. Circuit simulations show that for a 1 K-entry CBF with a 15-bit count per entry, L-CBF compared to S-CBF is 3.7 or 1.6 faster and requires 2.3 or 1.4 less energy depending on the operation. Additionally, this presents analytical energy and delay models for L-CBF. Our results demonstrate that for a variety of L-CBF organizations, the estimations by analytical models are within 5% and 10% of Spectre simulation results for delay and energy, respectively. INTRODUCTION Many architectural techniques have relied on hardware counting bloom filters (CBFs) to improve upon the power, delay, and complexity of various processor structures. For example, CBFs have been used to improve performance and power in snoop coherent multiprocessor or multi-core systems. CBFs have been also utilized to improve the scalability of load/store scheduling queues and to reduce instruction replays by assisting in early miss determination at the L1 data cache. In these applications, CBFs help eliminate broadcasts over the interconnection network in multiprocessor systems, CBFs also help reduce accesses to much larger and thus much slower and power-hungry content addressable memories, or cache tag arrays. In all above mentioned hardware applications, CBFs improve the energy and speed of membership tests. Checking whether a memory block is currently cached is an example of a membership test in processors. The CBF provides a definite answer for behavior determine how many membership tests can be serviced by the CBF. The second factor is the energy and delay characteristics of the CBF. The more membership tests are serviced by the CBF “alone” and the more speed and energy efficient the CBF implementation is, the higher the benefits. If the key distribution is not known, or too complicated to yield to analysis, then the use of a particular hash function may have adverse effects: it may magnify correlations among keys and fill the hash table non-uniformly. In universal hashing, one of several hash functions is chosen at random. Here, we see a different technique in Bloom filters, several hash functions are applied to each key. Again, this allows us to use simple hash functions while at the same time minimizing the effects of any particular hash function. The main purpose of Bloom filters is to build a space data structure for set membership. Indeed, to maximize space efficiency, correctness is sacrificed: if a given key is not in the set, then a Bloom filter may give the wrong answer (this is called a false positive), but the probability of such a wrong answer can be made small. A typical application of Bloom filters is web caching. An ISP may keep several levels of carefully located caches to speed up the loading of commonly viewed web pages, in particular for large data objects, such as images and videos. If a client requests a particular URL, then the service needs to determine quickly if the requested page is in one of its caches. False positives, while undesirable, are acceptable: if it turns out that a page thought to be in a cache is not there, it will be loaded from its native URL, and the penalty is not much worse than not having the cache in the first place. The significant contributions of this work are as follows. 1) It proposes L-CBF, a novel, energy and speed efficient implementation for CBFs. 2) It compares the energy, delay and area of two CBF implementations, L-CBF and S-CBF using their circuit level implementations and full-custom layouts in 0.13-m fabrication technology. 3) It presents analytical delay and energy models for L-CBF and compares the model estimations against simulation results. CBFs This section reviews CBFs and their characteristics. Additionally, it discusses the previously assumed implementation for the CBFs, which has not been investigated at the physical level. Introduction to CBFs: Figure 1: CBF as black box. 1) CBF as a Black Box: As shown in Fig. 1, a CBF is conceptually an array of counts indexed via a hash function of the element under membership test. A CBF has three operations:1) increment count (INC); 2) decrement count (DEC); and 3) test if the count is zero (PROBE). The first two operations increment or decrement the corresponding count by one, and the third one checks if the count is zero and returns true or false(single-bit output). We will refer to the first two operations as updates and to the third one as a probe. A CBF is characterized by its number of entries and the width of the count per entry. 2) CBF Characteristics: Membership tests using CBFs are performed by probe operations. In response to a V.Srilatha et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (6), 2012,5378-5383