AN EFFICIENT LOW-POWER CONTENT- ADDRESSABLE MEMORY USING COMPRESSOR MEMORY BLOCK

In this paper, we proposed a low-power content-addressable memory (CAM) employing a new algorithm for associativity between the input tag and the corresponding address of the output data. The proposed architecture is based on memory block. Given an input data the proposed architecture compares the stored data with input data and send the single matched data address as the output. Therefore, the dynamic energy consumption of the proposed design is significantly lower compared with that of a sparse Clustered network based CAM design. In this project we have shown as the effective error detection and correction in the data set. For detecting and correcting the data this project allows synergetic reuse COMPRESSOR MEMORY BLOCK.   For very high speed searching applications, Bloom filters has been proposed. Associative memory, associative storage and associative array are the synonyms of CAM. For programming in data structures the name associative array is used most. XILINX ISE was used for the simulation process. The search delay of the proposed design is less. So the speed is more as compared to that of SCN CAM design

Implementation Of “Bloom Filter” Using Cam Based Structure

content addressable memory (CAM) employing a new algorithm for associativity between the input tag and the corresponding address of the output data. The proposed architecture is based on a recently developed sparse clustered network using binary connections that on-average eliminates most of the parallel comparisons per- formed during a search. Therefore, the dynamic energy consumption of the proposed design is significantly lower compared with that of a conventional low-power CAM design. Given an input tag, the proposed architecture computes a few possibilities for the location of the matched tag and performs the comparisons on them to locate a single valid match. Bloom filters (BFs) provide a fast and efficient way to check whether a given element belongs to a set. The BFs are used in numerous applications, for example, in communications and networking. There is also ongoing research to extend and enhance BFs and to use them in new scenarios. Reliability is becoming a challenge for advanced electronic circuits as the number of errors due to manufacturing variations, radiation, and reduced noise margins increase as technology scales. In brief, it is shown that BFs can be used to detect and correct errors in their associated data set. This allows a synergetic reuse of existing BFs to also detect and correct errors. The proposed scheme Content-addressable memory (CAM) is a special type of computer memory used in certain very-high-speed searching applications. It is also known as associative memory, associative storage, or associative array, although the last term is more often used for a programming data structure. It compares input search data (tag) against a table of stored data, and returns the address of matching data (or in the case of associative memory, the matching data). Several custom computers, like the Goodyear STARAN, were built to implement CAM, and were designated associative computers

A scalable architecture for ordered parallelism

We present Swarm, a novel architecture that exploits ordered irregular parallelism, which is abundant but hard to mine with current software and hardware techniques. In this architecture, programs consist of short tasks with programmer-specified timestamps. Swarm executes tasks speculatively and out of order, and efficiently speculates thousands of tasks ahead of the earliest active task to uncover ordered parallelism. Swarm builds on prior TLS and HTM schemes, and contributes several new techniques that allow it to scale to large core counts and speculation windows, including a new execution model, speculation-aware hardware task management, selective aborts, and scalable ordered commits. We evaluate Swarm on graph analytics, simulation, and database benchmarks. At 64 cores, Swarm achieves 51--122× speedups over a single-core system, and out-performs software-only parallel algorithms by 3--18×.National Science Foundation (U.S.) (Award CAREER-145299

Design and Evaluation of Packet Classification Systems, Doctoral Dissertation, December 2006

Although many algorithms and architectures have been proposed, the design of efficient packet classification systems remains a challenging problem. The diversity of filter specifications, the scale of filter sets, and the throughput requirements of high speed networks all contribute to the difficulty. We need to review the algorithms from a high-level point-of-view in order to advance the study. This level of understanding can lead to significant performance improvements. In this dissertation, we evaluate several existing algorithms and present several new algorithms as well. The previous evaluation results for existing algorithms are not convincing because they have not been done in a consistent way. To resolve this issue, an objective evaluation platform needs to be developed. We implement and evaluate several representative algorithms with uniform criteria. The source code and the evaluation results are both published on a web-site to provide the research community a benchmark for impartial and thorough algorithm evaluations. We propose several new algorithms to deal with the different variations of the packet classification problem. They are: (1) the Shape Shifting Trie algorithm for longest prefix matching, used in IP lookups or as a building block for general packet classification algorithms; (2) the Fast Hash Table lookup algorithm used for exact flow match; (3) the longest prefix matching algorithm using hash tables and tries, used in IP lookups or packet classification algorithms;(4) the 2D coarse-grained tuple-space search algorithm with controlled filter expansion, used for two-dimensional packet classification or as a building block for general packet classification algorithms; (5) the Adaptive Binary Cutting algorithm used for general multi-dimensional packet classification. In addition to the algorithmic solutions, we also consider the TCAM hardware solution. In particular, we address the TCAM filter update problem for general packet classification and provide an efficient algorithm. Building upon the previous work, these algorithms significantly improve the performance of packet classification systems and set a solid foundation for further study

Algorithms and Architectures for Network Search Processors

The continuous growth in the Internet’s size, the amount of data traffic, and the complexity of processing this traffic gives rise to new challenges in building high-performance network devices. One of the most fundamental tasks performed by these devices is searching the network data for predefined keys. Address lookup, packet classification, and deep packet inspection are some of the operations which involve table lookups and searching. These operations are typically part of the packet forwarding mechanism, and can create a performance bottleneck. Therefore, fast and resource efficient algorithms are required. One of the most commonly used techniques for such searching operations is the Ternary Content Addressable Memory (TCAM). While TCAM can offer very fast search speeds, it is costly and consumes a large amount of power. Hence, designing cost-effective, power-efficient, and high-speed search techniques has received a great deal of attention in the research and industrial community. In this thesis, we propose a generic search technique based on Bloom filters. A Bloom filter is a randomized data structure used to represent a set of bit-strings compactly and support set membership queries. We demonstrate techniques to convert the search process into table lookups. The resulting table data structures are kept in the off-chip memory and their Bloom filter representations are kept in the on-chip memory. An item needs to be looked up in the off-chip table only when it is found in the on-chip Bloom filters. By filtering the off-chip memory accesses in this fashion, the search operations can be significantly accelerated. Our approach involves a unique combination of algorithmic and architectural techniques that outperform some of the current techniques in terms of cost-effectiveness, speed, and power-efficiency

Exploiting the Computational Power of Ternary Content Addressable Memory

Ternary Content Addressable Memory or in short TCAM is a special type of memory that can execute a certain set of operations in parallel on all of its words. Because of power consumption and relatively small storage capacity, it has only been used in special environments. Over the past few years its cost has been reduced and its storage capacity has increased signifi cantly and these exponential trends are continuing. Hence it can be used in more general environments for larger problems. In this research we study how to exploit its computational power in order to speed up fundamental problems and needless to say that we barely scratched the surface. The main problems that has been addressed in our research are namely Boolean matrix multiplication, approximate subset queries using bloom filters, Fixed universe priority queues and network flow classi cation. For Boolean matrix multiplication our simple algorithm has a run time of O (d(N^2)/w) where N is the size of the square matrices, w is the number of bits in each word of TCAM and d is the maximum number of ones in a row of one of the matrices. For the Fixed universe priority queue problems we propose two data structures one with constant time complexity and space of O((1/ε)n(U^ε)) and the other one in linear space and amortized time complexity of O((lg lg U)/(lg lg lg U)) which beats the best possible data structure in the RAM model namely Y-fast trees. Considering each word of TCAM as a bloom filter, we modify the hash functions of the bloom filter and propose a data structure which can use the information capacity of each word of TCAM more efi ciently by using the co-occurrence probability of possible members. And finally in the last chapter we propose a novel technique for network flow classi fication using TCAM

Techniques d'abstraction pour l'analyse et la mitigation des effets dus à la radiation

The main objective of this thesis is to develop techniques that can beused to analyze and mitigate the effects of radiation-induced soft errors in industrialscale integrated circuits. To achieve this goal, several methods have been developedbased on analyzing the design at higher levels of abstraction. These techniquesaddress both sequential and combinatorial SER.Fault-injection simulations remain the primary method for analyzing the effectsof soft errors. In this thesis, techniques which significantly speed-up fault-injectionsimulations are presented. Soft errors in flip-flops are typically mitigated by selectivelyreplacing the most critical flip-flops with hardened implementations. Selectingan optimal set to harden is a compute intensive problem and the second contributionconsists of a clustering technique which significantly reduces the number offault-injections required to perform selective mitigation.In terrestrial applications, the effect of soft errors in combinatorial logic hasbeen fairly small. It is known that this effect is growing, yet there exist few techniqueswhich can quickly estimate the extent of combinatorial SER for an entireintegrated circuit. The third contribution of this thesis is a hierarchical approachto combinatorial soft error analysis.Systems-on-chip are often developed by re-using design-blocks that come frommultiple sources. In this context, there is a need to develop and exchange reliabilitymodels. The final contribution of this thesis consists of an application specificmodeling language called RIIF (Reliability Information Interchange Format). Thislanguage is able to model how faults at the gate-level propagate up to the block andchip-level. Work is underway to standardize the RIIF modeling language as well asto extend it beyond modeling of radiation-induced failures.In addition to the main axis of research, some tangential topics were studied incollaboration with other teams. One of these consisted in the development of a novelapproach for protecting ternary content addressable memories (TCAMs), a specialtype of memory important in networking applications. The second supplementalproject resulted in an algorithm for quickly generating approximate redundant logicwhich can protect combinatorial networks against permanent faults. Finally anapproach for reducing the detection time for errors in the configuration RAM forField-Programmable Gate-Arrays (FPGAs) was outlined.Les effets dus à la radiation peuvent provoquer des pannes dans des circuits intégrés. Lorsqu'une particule subatomique, fait se déposer une charge dans les régions sensibles d'un transistor cela provoque une impulsion de courant. Cette impulsion peut alors engendrer l'inversion d'un bit ou se propager dans un réseau de logique combinatoire avant d'être échantillonnée par une bascule en aval.Selon l'état du circuit au moment de la frappe de la particule et selon l'application, cela provoquera une panne observable ou non. Parmi les événements induits par la radiation, seule une petite portion génère des pannes. Il est donc essentiel de déterminer cette fraction afin de prédire la fiabilité du système. En effet, les raisons pour lesquelles une perturbation pourrait être masquée sont multiples, et il est de plus parfois difficile de préciser ce qui constitue une erreur. A cela s'ajoute le fait que les circuits intégrés comportent des milliards de transistors. Comme souvent dans le contexte de la conception assisté par ordinateur, les approches hiérarchiques et les techniques d'abstraction permettent de trouver des solutions.Cette thèse propose donc plusieurs nouvelles techniques pour analyser les effets dus à la radiation. La première technique permet d'accélérer des simulations d'injections de fautes en détectant lorsqu'une faute a été supprimée du système, permettant ainsi d'arrêter la simulation. La deuxième technique permet de regrouper en ensembles les éléments d'un circuit ayant une fonction similaire. Ensuite, une analyse au niveau des ensemble peut être faite, identifiant ainsi ceux qui sont les plus critiques et qui nécessitent donc d'être durcis. Le temps de calcul est ainsi grandement réduit.La troisième technique permet d'analyser les effets des fautes transitoires dans les circuits combinatoires. Il est en effet possible de calculer à l'avance la sensibilité à des fautes transitoires de cellules ainsi que les effets de masquage dans des blocs fréquemment utilisés. Ces modèles peuvent alors être combinés afin d'analyser la sensibilité de grands circuits. La contribution finale de cette thèse consiste en la définition d'un nouveau langage de modélisation appelé RIIF (Reliability Information Ineterchange Format). Ce langage permet de décrire le taux des fautes dans des composants simples en fonction de leur environnement de fonctionnement. Ces composants simples peuvent ensuite être combinés permettant ainsi de modéliser la propagation de leur fautes vers des pannes au niveau système. En outre, l'utilisation d'un langage standard facilite l'échange de données de fiabilité entre les partenaires industriels.Au-delà des contributions principales, cette thèse aborde aussi des techniques permettant de protéger des mémoires associatives ternaires (TCAMs). Les approches classiques de protection (codes correcteurs) ne s'appliquent pas directement. Une des nouvelles techniques proposées consiste à utiliser une structure de données qui peut détecter, d'une manière statistique, quand le résultat n'est pas correct. La probabilité de détection peut être contrôlée par le nombre de bits alloués à cette structure. Une autre technique consiste à utiliser un détecteur de courant embarqué (BICS) afin de diriger un processus de fond directement vers le région touchée par une erreur. La contribution finale consiste en un algorithme qui permet de synthétiser de la logique combinatoire afin de protéger des circuits combinatoires contre les fautes transitoires.Dans leur ensemble, ces techniques facilitent l'analyse des erreurs provoquées par les effets dus à la radiation dans les circuits intégrés, en particulier pour les très grands circuits composés de blocs provenant de divers fournisseurs. Des techniques pour mieux sélectionner les bascules/flip-flops à durcir et des approches pour protéger des TCAMs ont étés étudiées

Fast Packet Processing on High Performance Architectures

The rapid growth of Internet and the fast emergence of new network applications have brought great challenges and complex issues in deploying high-speed and QoS guaranteed IP network. For this reason packet classication and network intrusion detection have assumed a key role in modern communication networks in order to provide Qos and security. In this thesis we describe a number of the most advanced solutions to these tasks. We introduce NetFPGA and Network Processors as reference platforms both for the design and the implementation of the solutions and algorithms described in this thesis. The rise in links capacity reduces the time available to network devices for packet processing. For this reason, we show different solutions which, either by heuristic and randomization or by smart construction of state machine, allow IP lookup, packet classification and deep packet inspection to be fast in real devices based on high speed platforms such as NetFPGA or Network Processors