Soft-Decoding in LDPC based SSD Controllers
Stephen Bates, Senior Technical Director, CSTO, PMC-Sierra
EETimes (12/7/2015 05:04 PM EST)
What happens when that initial decode fails? How soft data can be used to recover data on the SSD.
In the previous article in this series, I talked about how we can control the parameters of Low-Density Parity-Check (LDPC) error correction codes in order to manage the latency associated with reads from a Solid-State Drive (SSD). However, we only looked at the latency associated with a single decode of the LDPC codeword. In this post, we will take a look at what happens when that initial decode fails and how soft data can be used to recover data on the SSD.
Hard and soft data decoding
In October 1948, Claude Shannon published his seminal paper “A Mathematical Theory of Communication,” which kick-started the discipline of information theory. The work in this paper is still used today to determine how good or bad an error correction code is because it defined a performance bound beyond which no error correction code can go. Interestingly for any given channel there exists two bounds, one for decoding using hard data and one for decoding using soft data. A few examples of this are given in Table 1 below.
To read the full article, click here
Related Semiconductor IP
- NavIC LDPC Decoder
- Flash Memory LDPC Decoder IP Core
- DVB-S2X Wideband LDPC/ BCH Encoder
- eMMC LDPC Encoder/Decoder
- 1Gbit/s LDPC Decoder and Encoder (WiMedia UWB)
Related White Papers
- IP Verification : Integrating IP with assertion- based verification
- Multitasking operations require more hardware based RTOSes
- The role of sockets in platform based design: a case study of the OMAP platform
- System CoreWare Based Design using RapidChip Platform ASIC
Latest White Papers
- Fault Injection in On-Chip Interconnects: A Comparative Study of Wishbone, AXI-Lite, and AXI
- eFPGA – Hidden Engine of Tomorrow’s High-Frequency Trading Systems
- aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on RawAudio
- Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
- Hardware Acceleration of Kolmogorov-Arnold Network (KAN) in Large-Scale Systems