Using Embedded Dynamic Random Access Memory to Reduce Energy Consumption of Magnetic Recording Read Channel

• Published in: IEEE transactions on magnetics 2010-01, Vol.46 (1), p.87-91
• Main Authors: Ningde Xie, Tong Zhang, Haratsch, E.F.
• Format: Article
• Language: English
• Subjects: Costs; Cross-disciplinary physics: materials science; rheology; DRAM chips; Embedded dynamic random access memory (DRAM); Energy consumption; Exact sciences and technology; Integrated circuit manufacture; Iterative algorithms; Iterative methods; low-density parity check (LDPC); Magnetic recording; Materials science; Other topics in materials science; Physics; Random access memory; Signal detection; Silicon
• ISSN: 0018-9464; 1941-0069
• DOI: 10.1109/TMAG.2009.2026898

Although the performance of a magnetic recording read channel can be improved by employing advanced iterative signal detection and coding techniques, the method nevertheless tends to incur significant silicon area and energy consumption overhead. Motivated by recent significant improvement of high-density embedded dynamic random access memory (eDRAM) towards high manufacturability at low cost, we explored the potential of integrating eDRAM in read channel integrated circuits (IC) to minimize the silicon area and energy consumption cost incurred by iterative signal detection and coding. As a result of the memory-intensive nature of iterative signal detection and coding algorithms, the silicon cost can be reduced in a straightforward manner by directly replacing conventional SRAM with eDRAM. However, reducing the energy consumption may not be trivial. In this paper, we present two techniques that trade eDRAM storage capacity to reduce the energy consumption of iterative signal detection and coding datapath. We have demonstrated dDRAM's energy saving potential by designing a representative iterative read channel at the 65 nm technology node. Simulation shows that we can eliminate over 99.99% of post-processing computation for dominant error events detection, and achieve up to a 67% reduction of decoding energy consumption.