Zi-CAM: A Power and Resource Efficient Binary Content-Addressable Memory on FPGAs

Author:

Irfan MuhammadORCID,Ullah Zahid,C. C. Cheung Ray

Abstract

Content-addressable memory (CAM) is a type of associative memory, which returns the address of a given search input in one clock cycle. Many designs are available to emulate the CAM functionality inside the re-configurable hardware, field-programmable gate arrays (FPGAs), using static random-access memory (SRAM) and flip-flops. FPGA-based CAMs are becoming popular due to the rapid growth in software defined networks (SDNs), which uses CAM for packet classification. Emulated designs of CAM consume much dynamic power owing to a high amount of switching activity and computation involved in finding the address of the search key. In this paper, we present a power and resource efficient binary CAM architecture, Zi-CAM, which consumes less power and uses fewer resources than the available architectures of SRAM-based CAM on FPGAs. Zi-CAM consists of two main blocks. RAM block (RB) is activated when there is a sequence of repeating zeros in the input search word; otherwise, lookup tables (LUT) block (LB) is activated. Zi-CAM is implemented on Xilinx Virtex-6 FPGA for the size 64 × 36 which improved power consumption and hardware cost by 30 and 32%, respectively, compared to the available FPGA-based CAMs.

Funder

City University of Hong Kong

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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1. A network packet classification engine for real-time applications with enhanced performance;International Journal of Communication Networks and Distributed Systems;2024

2. Comparative Analysis of Power and Hardware Utilization in an Energy-Efficient 8:3 Encoder;2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC);2023-07-06

3. Using FPGA-based content-addressable memory for mnemonics instruction searching in assembler design;The Journal of Supercomputing;2023-05-07

4. A case study: Understanding The Nature of Memories Architectures in FPGAs to Built-up Bi-CAM;Mühendislik Bilimleri ve Araştırmaları Dergisi;2023-04-30

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