Introduction to Flash Memories And Flash Translation Layer

1. Introduction to Flash MemoriesAnd Flash Translation Layer 장보길 한국 외국어대학교 디지털정보공학과 System Software LAB xearo@hufs.ac.kr

2. Storage Technologies • Magnetic recording • Optical recording • Electronic memories

3. mobile devices embedded systems flash disks Applications of Flash Memories • Flash memory is the major type of NVM (more than 90% of NVM market)

4. What is a flash memory cell?

6. Physics of Flash Memory Cell array in a flash memory A flash memory cell (Floating gate) • A flash memory cell can store charge. And the charge level represents data.

7. How does a cell store a bit? 0

8. Inject electrons:Hot electron injection mechanism, or Fowler-Nordheim tunneling mechanism • Remove electrons:Fowler-Nordheim tunneling mechanism How does a cell store a bit? 1

9. 1 cell Single-level cell and Multi-level cell • Single-level cell: Two levels  One bit 0 cell • Multi-level cell:q levels  bits 0 1 2 q-1 cell cell cell cell Typical number of cell levels: 2, 4, 8, 16

10. Source: [Bandyopadhyay, Serrano, Hasler 2005] How is a cell programmed? • Through multiple rounds of charge injection Target level A flash cell

11. Speed and physical limits Speed of operations: Read: Fast Write: Slower (due to multiple rounds of programming) Erase: Very slow Physical limits: Endurance. (In NOR, a block can stand about 100,000 to 1,000,000 erasures. In NAND, it can stand 10,000 to 100,000 erasures.) Physical size (e.g., 34nm). Voltage. Number of electrons.

12. NOR and NAND Flash Memories NOR: Older, still used. NAND: Newer, much more popular now.

13. Word line Control gate Oxidelayer (-) (-) (-) (-) Floating gate Drain Source Bit line flash memory cell What is a NOR flash memory • Cells form blocks. A block has about 100,000 cells. 2. NOR is a random-access device. Every cell is directly addressable by the processor. That is, a cell can be individually read and programmed.

14. What is a NOR flash memory Block erasure!!!!!!

15. Block erasure In NOR, the level of a cell can be increased individually and multiple times. But to lower any cell level, the whole block must be erased at the same time. Block block of cells

16. What is a NAND flash memory Cells form blocks. Every block is an array. Every row is a page. Block page page Typically: 32 to 128 rows (pages) page page Read and write: A page as a unit. Typically: 512 to 2048 cells in a row (page) Block erasure!!!!!!

17. Writing a page in NAND A page can be written only once before the block is erased. It is even recommended that the pages are written sequentially. Page 1 Partial writing: A page is partitioned into 4 parts, and we can write a part at a time. Page 2 ………… Page 64 Part 1 Part 2 Part 3 Part 4 A page Note: This is logic partition. Why?: Programming is not very accurate, especially with multiple times of writing (for the same page, and for the interference between pages).

18. A typical NAND page with spare bytes 64 Bytes of spare area A page: 2KB of data Metadata ECC Undefined bits

19. Comparison of NOR & NAND flash Basic difference: Different ways to connect cells in a block. Additional difference: Ways to inject charge, used voltages. NOR: cells are independent NAND: Cells in the same column are connected (and disturb each other).

20. Comparison of NOR & NAND flash NOR: NAND: 1. Lower density. 1. Higher density. 2. Random access. 2. Page access. 3. More reliable. 3. Less reliable, error-prone. (Requires ECCs.) 4. Slower erase. 4. Faster erase. 5. Faster random read. 5. Faster streaming read. 6. Mainly to store code. 6. Mainly to store data.

21. Flash File System Wear leveling Garbage collection Mapping

22. Wear leveling Wear leveling: Let the blocks be erased about the same number of times. Method: Write data in different places (instead of the same block). How to know the block’s level of wearing out: Count the number of erasures, or Measure the performance of its cells (e.g., erase latencies), or Other methods? Alternative approach: Just use randomization (i.e., randomly use the blocks, and hopefully, things will even out).

23. Wear leveling techniques Simple case: If all the data in a block are obsolete, just erase it. Write in blocks that are less worn out. What if the blocks contain both obsolete and valid data? Page 1: valid Page 2: obsolete Page 3: obsolete Page 4: obsolete ………… Page 64: valid

24. Combining wear leveling with garbage collection This happens when we want to re-use those blocks that contain both valid and invalid (obsolete) data. Approaches: • Use a cost/benefit ratio to decide which block to erase. • (Before erasing it, the valid data need to be moved first.) (2) Store frequently-changing data together, and store data that do not change much together. (Reason: After a while, in a block containing frequently-changing data, most of the data are probably already invalid.) (3) Many heuristic approaches. (And many patents.) Most important: Design it based on the application.

25. When garbage collection is done • Garbage collection(of blocks) can happen when: • As background work, i.e., when CPU is idle; or • (2) On demand, i.e., when there is not enough free space.

26. Mapping How to use flash memories to store data? One approach: Treat the flash memory as a block device, much like disk sectors. Advantage: Allow standard file systems to use flash. Problems with a simple linear mapping from virtual blocks to flash-memory pages: Some blocks can be erased too often. Unable (or inefficient) to write data smaller than a flash block. Solution: Wear leveling (that is, to move data around). Mapping between virtual blocks and physical pages is needed. The spare part in a page may have bits indicating if the page is free/used or valid/obsolete.

27. Mapping Direct map Virtual blocks Physical pages Stored in RAM, or partially in RAM and partially in flash. Inverse map Physical pages Virtual blocks Stored in flash. • Flash Translation Layer (FTL): • A technique to • store some of the direct map in flash, and • reduce the cost of updating the maps stored in flash.

28. Flash file systems Tens of flash file systems (FFS) have been designed. Clearly, more of them will be designed…… A flash file system: Is a data structure that represents a collection of mutable random-access files in a hierarchical name space. Provides the block mapping technique. Does wear leveling and garbage collection. Maybe design a different, more flash-specific file system? Do the same for data structures, such as B-trees and R-trees.

29. More flash-specific file systems Most of the flash-specific file systems use the same overall principle, that of a log-structured file system. Why? It is easier to record the small changes (and write them down sequentially), than to rewrite the whole file.

30. What happens when the blocks are read/erased/written again and again…

31. Disturb mechanisms Write/read disturb: When a cell is programmed (or read), the cells in the same column/row are softly programmed. For some MLC, it is even recommended that after reading the same page 1000 times, write the clean data back again.

32. Errors, Signal processing, and ECCs When a block is erased, its quality goes down. The rate of errors increases… Types of errors: Random errors. Fixed-position errors (because the cells really become defected). Cells in the same column can become bad together. Ways to correct errors: Signal processing ECCs (Hamming, BCH codes.) (Reed-Solomn codes? LDPC codes? Under study.)

33. New Area in Information Theory: Coding for Flash Memories

34. Summary of recent results Rewriting codes: Worst-case performance:[Jiang,Bohossian,Bruck,ISIT’07], [Bohossian,Jiang,Bruck,ISIT’07], [Jiang,Bruck,ISIT’08], [Yaakobi,Siegel,Vardy,Wolf,Allerton’08], [Jiang,Langberg,Schwartz,Bruck,ISIT’09], [Mahdavifar,Siegel,Vardy,Wolf,Yaakobi,ISIT'09] Expected performance: [Finucane,Liu,Mitzenmacher,Allerton’08], [Jiang,Langberg,Schwartz,Bruck,ISIT’09] Rank modulation: Rewriting: [Jiang,Mateescu,Schwartz,Bruck,ISIT’08] Error-correction codes: [Jiang,Schwartz,Bruck,ISIT’08] Sequences: [Jiang,Mateescu,Schwartz,Bruck,ISIT’08], [Wang,Jiang,Burck,ISIT’09] Capacity: [Jiang,Bruck,ISITA’08], [Lastras-Montano,Franceschini,Mittelholzer,Sharma,ISITA'08], [Lastras-Montano,Franceschini,Mittelholzer,Karidis,Wegman,ISIT'09], [Jiang,Li,PACRIM’09] Error correcting/scrubbing codes: [Cassuto,Schwartz,Bohossian,Bruck,ISIT’07], [Jiang,ISIT’07], [Jiang,Li,Wang,CWIT’09] Data movement: [Jiang,Mateescu,Yaakobi,Bruck,Siegel,Vardy,Wolf,ISIT’09], [Jiang,Langberg,Mateescu,Bruck,Allerton’09]

35. Rewriting codes WOM (write-once memory) code Floating code: Joint coding of multiple variables Example: 2 bits are stored in 3 cells with 4 levels. Every time one bit is changed. How many rewrites can be supported? 1,1 0,1 1,1 1,0 0,1 0,0 1,0 0,0 Now use floating codes.

36. Floating codes Example: 2 bits are stored in 3 cells with 4 levels. Every time one bit is changed. cell levels data

37. 3 writes 7 writes Rewriting codes WOM (write-once memory) code Floating code: Joint coding of multiple variables Example: 2 bits are stored in 3 cells with 4 levels. Every time one bit is changed. How many rewrites can be supported? 1,1 0,1 0,0 1,0 Now use floating codes. 1,1 0,1 0,0 1,0

38. times better times better Floating codes When two binary variables are stored in n cells of q levels, an optimal floating code can support rewrites. When k variables of alphabet size L are stored in n cells of q levels, the number of rewrites that a floating code can support is: If n is large  rewrites If k,L are large  Roughly rewrites No coding  Roughly rewrites

39. More general model for rewriting Floating codes: Every rewrite changes one variable. 011 111 State transitions of data 001 101 Example: 3 binary variables Hypercube 010 110 000 100 Buffer codes: Remember most recent data. [BJB’07] State transitions of data: De Bruijn graph 011010100010010010111101 More general: [JLSB’09] Maximum degree: The data change in a bounded-degree graph.

40. Trajectory code for bounded-degree rewrite Model: The state-transition diagram of the data has bounded degree . This code is asymptotically optimal. [Jiang, Langberg, Schwartz, Bruck, ISIT’09]

41. Rank Modulation [J, Mateescu, Schwartz, Bruck, ISIT’08]

42. Cell Programming • Noisy, monotonic • Trend: more levels, smaller cells • Question: How to write data reliably when cells cannot be programmed reliably? • Challenges: overshoot, worst-case constraint. • Approach: adaptive cell-ensemble programming. • Rank modulation is such an approach.

43. Rank Modulation • Analog cell levels induce permutations. Example: 3 cells can induce 3!=6 permutations • Permutations represent data. • Method of programming: from low to high. • Advantage: no overshoot, adaptive coding. 132 213 231 312 321 123

44. Rewriting; Error correction • Rewrite: How to rewrite data in the rank modulation scheme? • Error correction: How to design error-correcting codes? What does error mean?

45. A few other topics: (3) Data movement Block 1 Block 2 Block n Empty block erasures are needed. No coding: erasures are needed. With coding: [Jiang, Mateescu, Yaakobi, Bruck, Siegel, Vardy, Wolf, ISIT’09]

46. Summary of recent results Rewriting codes: Worst-case performance:[Jiang,Bohossian,Bruck,ISIT’07], [Bohossian,Jiang,Bruck,ISIT’07], [Jiang,Bruck,ISIT’08], [Yaakobi,Siegel,Vardy,Wolf,Allerton’08], [Jiang,Langberg,Schwartz,Bruck,ISIT’09], [Mahdavifar,Siegel,Vardy,Wolf,Yaakobi,ISIT'09] Expected performance: [Finucane,Liu,Mitzenmacher,Allerton’08], [Jiang,Langberg,Schwartz,Bruck,ISIT’09] Rank modulation: Rewriting: [Jiang,Mateescu,Schwartz,Bruck,ISIT’08] Error-correction codes: [Jiang,Schwartz,Bruck,ISIT’08] Sequences: [Jiang,Mateescu,Schwartz,Bruck,ISIT’08], [Wang,Jiang,Burck,ISIT’09] Capacity: [Jiang,Bruck,ISITA’08], [Lastras-Montano,Franceschini,Mittelholzer,Sharma,ISITA'08], [Lastras-Montano,Franceschini,Mittelholzer,Karidis,Wegman,ISIT'09], [Jiang,Li,PACRIM’09] Error correcting/scrubbing codes: [Cassuto,Schwartz,Bohossian,Bruck,ISIT’07], [Jiang,ISIT’07], [Jiang,Li,Wang,CWIT’09] Data movement: [Jiang,Mateescu,Yaakobi,Bruck,Siegel,Vardy,Wolf,ISIT’09], [Jiang,Langberg,Mateescu,Bruck,Allerton’09]