Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

Field Programmable Gate Array Applications—A Scientometric Review

rDBF: A r-Dimensional Bloom Filter for massive scale membership query

Membership determination of text strings has been an important procedure for analyzing textual data of a tremendous amount, especially when time is a crucial factor. Bloom filter has been a well-known approach for dealing with such a problem because of its succinct structure and simple determination procedure. As determination of membership with classification is becoming increasingly desirable, parallel Bloom filters are often implemented for facilitating the additional classification requirement. The parallel Bloom filters, however, tend to produce additional false-positive errors since membership determination must be performed on each of the parallel layers. We propose a scheme based on CMAC, a neural network mapping, which only requires a single-layer calculation to simultaneously obtain information of both the membership and classification. A hash function specifically designed for text strings is also proposed. The proposed scheme could effectively reduce false-positive errors by converging the range of membership acceptance to the minimum for each class during the neural network mapping. Simulation results show that the proposed scheme committed significantly less errors than the benchmark, parallel Bloom filters, with limited and identical memory usage at different classification levels.

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A CMAC-based scheme for determining membership with classification of text strings

Randomness testing of non-cryptographic hash functions for real-time hash table based storage and look-up of URLs

Hash table is used in many areas of networking such as route lookup, packet classification, per-flow state management and network monitoring for its constant access time latency at moderate loads. However, collisions may become frequent at high loads in traditional hash tables, which may lead the access time complexity to be linear and intolerable to applications like high-speed route lookups. While some schemes were proposed to help resolve this problem and most of them may achieve O(1) average memory access per lookup, very few of them are able to cut down the access to a deterministic single one. In this paper, we design a structure called deterministic and efficient hash table (DEHT). In DEHT, a novel data structure on on-chip memory is built, with the help of which the off-chip memory access can be decreased to a single one at most per lookup even when the load of the hash table is very high. What's more, the on-chip data structure also plays a similar role as Bloom Filter to do membership screening, which can avoid most lookups of nonexistent items of the hash table visiting the off-chip memory. Through theoretical analysis and simulations, we show that our scheme is faster than other schemes in lookup operations; the usable load of the off-chip hash table, the memory efficiency and the false positive rate of the on-chip data structure are also favorable.

Rui Li

Papers

Fast and deterministic hash table lookup using discriminative bloom filters