CS252Graduate Computer Architecture

Lecture 16

Memory Technology (Con’t)Error Correction Codes

John Kubiatowicz

Electrical Engineering and Computer Sciences

University of California, Berkeley

http://www.eecs.berkeley.edu/~kubitron/cs252

3/30/2009 cs252-S09, Lecture 16 2

Review: 12 Advanced Cache Optimizations

• Reducing hit time

1.Small and simple caches

2.Way prediction

3.Trace caches

• Increasing cache bandwidth

4.Pipelined caches

5.Multibanked caches

6.Nonblocking caches

• Reducing Miss Penalty

7. Critical word first

8. Merging write buffers

• Reducing Miss Rate

9. Victim Cache

10. Hardware prefetching

11. Compiler prefetching

12. Compiler Optimizations

3/30/2009 cs252-S09, Lecture 16 3

Review: Main Memory Background

• Performance of Main Memory: – Latency: Cache Miss Penalty

» Access Time: time between request and word arrives

» Cycle Time: time between requests

– Bandwidth: I/O & Large Block Miss Penalty (L2)

• Main Memory is DRAM: Dynamic Random Access Memory– Dynamic since needs to be refreshed periodically (8 ms, 1% time)

– Addresses divided into 2 halves (Memory as a 2D matrix):

» RAS or Row Address Strobe

» CAS or Column Address Strobe

• Cache uses SRAM: Static Random Access Memory– No refresh (6 transistors/bit vs. 1 transistor

Size: DRAM/SRAM 4-8, Cost/Cycle time: SRAM/DRAM 8-16

3/30/2009 cs252-S09, Lecture 16 4

DRAM Architecture

Row

Addre

ss

Deco

der

Col.1

Col.2M

Row 1

Row 2N

Column Decoder & Sense Amplifiers

M

N

N+M

bit linesword lines

Memory cell(one bit)

DData

• Bits stored in 2-dimensional arrays on chip

• Modern chips have around 4 logical banks on each chip

– each logical bank physically implemented as many smaller arrays

3/30/2009 cs252-S09, Lecture 16 5

Review:1-T Memory Cell (DRAM)

• Write:– 1. Drive bit line

– 2.. Select row

• Read:– 1. Precharge bit line to Vdd/2

– 2.. Select row

– 3. Cell and bit line share charges

» Very small voltage changes on the bit line

– 4. Sense (fancy sense amp)

» Can detect changes of ~1 million electrons

– 5. Write: restore the value

• Refresh– 1. Just do a dummy read to every cell.

row select

bit

3/30/2009 cs252-S09, Lecture 16 6

DRAM Capacitors: more capacitance in a small area

• Trench capacitors:– Logic ABOVE capacitor– Gain in surface area of capacitor– Better Scaling properties– Better Planarization

• Stacked capacitors– Logic BELOW capacitor

– Gain in surface area of capacitor

– 2-dim cross-section quite small

3/30/2009 cs252-S09, Lecture 16 7

DRAM Operation: Three Steps• Precharge

– charges bit lines to known value, required before next row access

• Row access (RAS)– decode row address, enable addressed row (often multiple Kb in row)– bitlines share charge with storage cell– small change in voltage detected by sense amplifiers which latch

whole row of bits– sense amplifiers drive bitlines full rail to recharge storage cells

• Column access (CAS)– decode column address to select small number of sense amplifier

latches (4, 8, 16, or 32 bits depending on DRAM package)– on read, send latched bits out to chip pins– on write, change sense amplifier latches. which then charge storage

cells to required value– can perform multiple column accesses on same row without another

row access (burst mode)

3/30/2009 cs252-S09, Lecture 16 8

AD

OE_L

256K x 8DRAM9 8

WE_LCAS_LRAS_L

OE_L

A Row Address

WE_L

Junk

Read AccessTime

Output EnableDelay

CAS_L

RAS_L

Col Address Row Address JunkCol Address

D High Z Data Out

DRAM Read Cycle Time

Early Read Cycle: OE_L asserted before CAS_L Late Read Cycle: OE_L asserted after CAS_L

• Every DRAM access begins at:

– The assertion of the RAS_L

– 2 ways to read: early or late v. CAS

Junk Data Out High Z

DRAM Read Timing (Example)

3/30/2009 cs252-S09, Lecture 16 9

• DRAM (Read/Write) Cycle Time >> DRAM (Read/Write) Access Time

– 2:1; why?

• DRAM (Read/Write) Cycle Time :– How frequent can you initiate an access?

– Analogy: A little kid can only ask his father for money on Saturday

• DRAM (Read/Write) Access Time:– How quickly will you get what you want once you initiate an access?

– Analogy: As soon as he asks, his father will give him the money

• DRAM Bandwidth Limitation analogy:– What happens if he runs out of money on Wednesday?

TimeAccess Time

Cycle Time

Main Memory Performance

3/30/2009 cs252-S09, Lecture 16 10

Access Pattern without Interleaving:

Start Access for D1

CPU Memory

Start Access for D2

D1 available

Access Pattern with 4-way Interleaving:

Acc

ess

Ban

k 0

Access Bank 1

Access Bank 2

Access Bank 3

We can Access Bank 0 again

CPU

MemoryBank 1

MemoryBank 0

MemoryBank 3

MemoryBank 2

Increasing Bandwidth - Interleaving

3/30/2009 cs252-S09, Lecture 16 11

• Simple: – CPU, Cache, Bus, Memory

same width (32 bits)

• Interleaved: – CPU, Cache, Bus 1 word:

Memory N Modules(4 Modules); example is word interleaved

• Wide: – CPU/Mux 1 word;

Mux/Cache, Bus, Memory N words (Alpha: 64 bits & 256 bits)

Main Memory Performance

3/30/2009 cs252-S09, Lecture 16 12

• Timing model– 1 to send address,

– 4 for access time, 10 cycle time, 1 to send data

– Cache Block is 4 words

• Simple M.P. = 4 x (1+10+1) = 48• Wide M.P. = 1 + 10 + 1 = 12• Interleaved M.P. = 1+10+1 + 3 =15

address

Bank 0

048

12

address

Bank 1

159

13

address

Bank 2

26

1014

address

Bank 3

37

1115

Main Memory Performance

3/30/2009 cs252-S09, Lecture 16 13

Avoiding Bank Conflicts

• Lots of banksint x[256][512];

for (j = 0; j < 512; j = j+1)for (i = 0; i < 256; i = i+1)

x[i][j] = 2 * x[i][j];• Even with 128 banks, since 512 is multiple of 128, conflict on

word accesses

• SW: loop interchange or declaring array not power of 2 (“array padding”)

• HW: Prime number of banks– bank number = address mod number of banks

– bank number = address mod number of banks

– address within bank = address / number of words in bank

– modulo & divide per memory access with prime no. banks?

3/30/2009 cs252-S09, Lecture 16 14

Finding Bank Number and Address within a bank

• Problem: Determine the number of banks, Nb and the number of words in each bank, Wb, such that:

– given address x, it is easy to find the bank where x will be found, B(x), and the address of x within the bank, A(x).

– for any address x, B(x) and A(x) are unique– the number of bank conflicts is minimized

• Solution: Use the following relation to determine B(x) and A(x):B(x) = x MOD Nb

A(x) = x MOD Wb where Nb and Wb are co-prime (no factors)– Chinese Remainder Theorem shows that B(x) and A(x) unique.

• Condition is satisfied if Nb is prime of form 2m-1:– Since 2k

= 2k-m (2m-1) + 2k-m

2k MOD Nb = 2k-m MOD Nb=…= 2j with j< m– And, remember that: (A+B) MOD C = [(A MOD C)+(B MOD C)] MOD C

• Simple circuit for x mod Nb

– for every power of 2, compute single bit MOD (in advance)

– B(x) = sum of these values MOD Nb (low complexity circuit, adder with ~ m bits)

3/30/2009 cs252-S09, Lecture 16 15

Quest for DRAM Performance

1. Fast Page mode – Add timing signals that allow repeated accesses to row buffer

without another row access time– Such a buffer comes naturally, as each array will buffer 1024 to

2048 bits for each access

2. Synchronous DRAM (SDRAM)– Add a clock signal to DRAM interface, so that the repeated

transfers would not bear overhead to synchronize with DRAM controller

3. Double Data Rate (DDR SDRAM)– Transfer data on both the rising edge and falling edge of the

DRAM clock signal doubling the peak data rate– DDR2 lowers power by dropping the voltage from 2.5 to 1.8

volts + offers higher clock rates: up to 400 MHz– DDR3 drops to 1.5 volts + higher clock rates: up to 800 MHz

• Improved Bandwidth, not Latency

3/30/2009 cs252-S09, Lecture 16 16

Fast Memory Systems: DRAM specific• Multiple CAS accesses: several names (page mode)

– Extended Data Out (EDO): 30% faster in page mode

• Newer DRAMs to address gap; what will they cost, will they survive?

– RAMBUS: startup company; reinvented DRAM interface

» Each Chip a module vs. slice of memory

» Short bus between CPU and chips

» Does own refresh

» Variable amount of data returned

» 1 byte / 2 ns (500 MB/s per chip)

– Synchronous DRAM: 2 banks on chip, a clock signal to DRAM, transfer synchronous to system clock (66 - 150 MHz)

» DDR DRAM: Two transfers per clock (on rising and falling edge)

– Intel claims FB-DIMM is the next big thing

» Stands for “Fully-Buffered Dual-Inline RAM”

» Same basic technology as DDR, but utilizes a serial “daisy-chain” channel between different memory components.

3/30/2009 cs252-S09, Lecture 16 17

Fast Page Mode Operation• Regular DRAM Organization:

– N rows x N column x M-bit– Read & Write M-bit at a time– Each M-bit access requires

a RAS / CAS cycle

• Fast Page Mode DRAM– N x M “SRAM” to save a row

• After a row is read into the register

– Only CAS is needed to access other M-bit blocks on that row

– RAS_L remains asserted while CAS_L is toggled

N r

ows

N cols

DRAM

ColumnAddress

M-bit OutputM bits

N x M “SRAM”

RowAddress

A Row Address

CAS_L

RAS_L

Col Address Col Address

1st M-bit Access

Col Address Col Address

2nd M-bit 3rd M-bit 4th M-bit

3/30/2009 cs252-S09, Lecture 16 18

SDRAM timing (Single Data Rate)

• Micron 128M-bit dram (using 2Meg16bit4bank ver)– Row (12 bits), bank (2 bits), column (9 bits)

RAS(New Bank)

CAS Prechargex

BurstREADCAS Latency

3/30/2009 cs252-S09, Lecture 16 19

Double-Data Rate (DDR2) DRAM

[ Micron, 256Mb DDR2 SDRAM datasheet ]

Row Column Precharge Row’

Data

200MHz Clock

400Mb/s Data Rate

3/30/2009 cs252-S09, Lecture 16 20

DDR vs DDR2 vs DDR3

• All about increasing the rate at the pins

• Not an improvement in latency

– In fact, latency can sometimes be worse

• Internal banks often consumed for increased bandwidth

3/30/2009 cs252-S09, Lecture 16 21

DRAM name based on Peak Chip Transfers / SecDIMM name based on Peak DIMM MBytes / Sec

Stan-dard

Clock Rate (MHz)

M transfers / second

DRAM Name

Mbytes/s/ DIMM

DIMM Name

DDR 133 266 DDR266 2128 PC2100

DDR 150 300 DDR300 2400 PC2400

DDR 200 400 DDR400 3200 PC3200

DDR2 266 533 DDR2-533 4264 PC4300

DDR2 333 667 DDR2-667 5336 PC5300

DDR2 400 800 DDR2-800 6400 PC6400

DDR3 533 1066 DDR3-1066 8528 PC8500

DDR3 666 1333 DDR3-1333 10664 PC10700

DDR3 800 1600 DDR3-1600 12800 PC12800x 2 x 8

3/30/2009 cs252-S09, Lecture 16 22

DRAM Packaging

• DIMM (Dual Inline Memory Module) contains multiple chips arranged in “ranks”

• Each rank has clock/control/address signals connected in parallel (sometimes need buffers to drive signals to all chips), and data pins work together to return wide word

– e.g., a rank could implement a 64-bit data bus using 16x4-bit chips, or a 64-bit data bus using 8x8-bit chips.

• A modern DIMM usually has one or two ranks (occasionally 4 if high capacity)

– A rank will contain the same number of banks as each constituent chip (e.g., 4-8)

Address lines multiplexed row/column address

Clock and control signals

Data bus(4b,8b,16b,32b)

DRAM chip

~12

~7

3/30/2009 cs252-S09, Lecture 16 23

DRAM Channel

16Chip

Bank

16Chip

Bank

16Chip

Bank

16Chip

Bank

64-bit Data Bus

Command/Address Bus

Memory Controller

Rank

16Chip

Bank

16Chip

Bank

16Chip

Bank

16Chip

Bank

Rank

3/30/2009 cs252-S09, Lecture 16 24

FB-DIMM Memories

• Uses Commodity DRAMs with special controller on actual DIMM board

• Connection is in a serial form:FB

-DIM

M

FB

-DIM

M

FB

-DIM

M

FB

-DIM

M

FB

-DIM

M

Controller

FB-DIMM

RegularDIMM

3/30/2009 cs252-S09, Lecture 16 25

FLASH Memory

• Like a normal transistor but:– Has a floating gate that can hold charge– To write: raise or lower wordline high enough to cause charges to tunnel– To read: turn on wordline as if normal transistor

» presence of charge changes threshold and thus measured current

• Two varieties: – NAND: denser, must be read and written in blocks– NOR: much less dense, fast to read and write

Samsung 2007:16GB, NAND Flash

3/30/2009 cs252-S09, Lecture 16 26

Phase Change memory (IBM, Samsung, Intel)

• Phase Change Memory (called PRAM or PCM)– Chalcogenide material can change from amorphous to crystalline

state with application of heat– Two states have very different resistive properties – Similar to material used in CD-RW process

• Exciting alternative to FLASH– Higher speed– May be easy to integrate with CMOS processes

3/30/2009 cs252-S09, Lecture 16 27

• Tunneling Magnetic Junction RAM (TMJ-RAM)– Speed of SRAM, density of DRAM, non-volatile (no refresh)– “Spintronics”: combination quantum spin and electronics– Same technology used in high-density disk-drives

Tunneling Magnetic Junction

3/30/2009 cs252-S09, Lecture 16 28

• Motivation:– DRAM is dense Signals are easily disturbed– High Capacity higher probability of failure

• Approach: Redundancy– Add extra information so that we can recover from errors– Can we do better than just create complete copies?

• Block Codes: Data Coded in blocks– k data bits coded into n encoded bits– Measure of overhead: Rate of Code: K/N – Often called an (n,k) code– Consider data as vectors in GF(2) [ i.e. vectors of bits ]

• Code Space is set of all 2n vectors, Data space set of 2k vectors

– Encoding function: C=f(d)– Decoding function: d=f(C’)– Not all possible code vectors, C, are valid!

Big storage (such as DRAM/DISK):Potential for Errors!

3/30/2009 cs252-S09, Lecture 16 29

Error Correction Codes (ECC)• Memory systems generate errors (accidentally flipped-

bits)– DRAMs store very little charge per bit

– “Soft” errors occur occasionally when cells are struck by alpha particles or other environmental upsets.

– Less frequently, “hard” errors can occur when chips permanently fail.

– Problem gets worse as memories get denser and larger

• Where is “perfect” memory required?– servers, spacecraft/military computers, ebay, …

• Memories are protected against failures with ECCs

• Extra bits are added to each data-word– used to detect and/or correct faults in the memory system

– in general, each possible data word value is mapped to a unique “code word”. A fault changes a valid code word to an invalid one - which can be detected.

3/30/2009 cs252-S09, Lecture 16 30

Code Space

v0

C0=f(v0)

Code Distance(Hamming Distance)

General Idea: Code Vector Space

• Not every vector in the code space is valid• Hamming Distance (d):

– Minimum number of bit flips to turn one code word into another

• Number of errors that we can detect: (d-1)• Number of errors that we can fix: ½(d-1)

3/30/2009 cs252-S09, Lecture 16 31

Some Code Types• Linear Codes:

Code is generated by G and in null-space of H– (n,k) code: Data space 2k, Code space 2n

– (n,k,d) code: specify distance d as well

• Random code:– Need to both identify errors and correct them– Distance d correct ½(d-1) errors

• Erasure code:– Can correct errors if we know which bits/symbols are bad– Example: RAID codes, where “symbols” are blocks of disk– Distance d correct (d-1) errors

• Error detection code:– Distance d detect (d-1) errors

• Hamming Codes– d = 3 Columns nonzero, Distinct– d = 4 Columns nonzero, Distinct, Odd-weight

• Binary Golay code: based on quadratic residues mod 23– Binary code: [24, 12, 8] and [23, 12, 7]. – Often used in space-based schemes, can correct 3 errors

CHS dGC

3/30/2009 cs252-S09, Lecture 16 32

Hamming Bound, symbols in GF(2)• Consider an (n,k) code with distance d

– How do n, k, and d relate to one another?

• First question: How big are spheres?– For distance d, spheres are of radius ½ (d-1),

» i.e. all error with weight ½ (d-1) or less must fit within sphere

– Thus, size of sphere is at least:1 + Num(1-bit err) + Num(2-bit err) + …+ Num( ½(d-1) – bit err)

• Hamming bound reflects bin-packing of spheres:– need 2k of these spheres within code space

)1(2

1

0

d

e e

nSize

nd

e

k

e

n22

)1(2

1

0

3,2)1(2 dn nk

3/30/2009 cs252-S09, Lecture 16 33

How to Generate code words?• Consider a linear code. Need a Generator Matrix.

– Let vi be the data value (k bits), Ci be resulting code (n bits):

• Are there 2k unique code values?– Only if the k columns of G are linearly independent!

• Of course, need some way of decoding as well.

– Is this linear??? Why or why not?

• A code is systematic if the data is directly encoded within the code words.

– Means Generator has form:– Can always turn non-systematic

code into a systematic one (row ops)

'idi Cfv

ii vC G

P

IG

G must be an nk matrix

3/30/2009 cs252-S09, Lecture 16 34

Implicitly Defining Codes by Check Matrix• But – what is the distance of the code? Not obvious

• Instead, consider a parity-check matrix H (n[n-k])– Compute the following syndrome Si given code element Ci:

– Define valid code words Ci as those that give Si=0 (null space of H)

– Size of null space? (n-rank H)=k if (n-k) linearly independent columns in H

• Suppose you transmit code word C, and there is an error. Model this as vector E which flips selected bits of C to get R (received):

• Consider what happens when we multiply by H:

• What is distance of code?– Code has distance d if no sum of d-1 or less columns yields 0

– I.e. No error vectors, E, of weight < d have zero syndromes

– Code design: Design H matrix with these properties

ii CS H

ECR

EECRS HHH )(

Error vector

3/30/2009 cs252-S09, Lecture 16 35

How to relate G and H (Binary Codes)• Defining H makes it easy to understand distance of

code, but hard to generate code (H defines code implicitly!)

• However, let H be of following form:

• Then, G can be of following form (maximal code size):

• Notice: G generates values in null-space of H

IPH | P is (n-k)k, I is (n-k)(n-k)Result: H is (n-k)k

P

IG P is (n-k)k, I is kk

Result: G is nk

0|

iii vvS

P

IIPGH

3/30/2009 cs252-S09, Lecture 16 36

Simple example (Parity, D=2)• Parity code (8-bits):

• Note: Complexity of logic depends on number of 1s in row!

111111111H

11111111

10000000

01000000

00100000

00010000

00001000

00000100

00000010

00000001

G

v7

v6

v5

v4

v3

v2

v1

v0

+ c8

+ s0

C8

C7

C6

C5

C4

C3

C2

C1

C0

3/30/2009 cs252-S09, Lecture 16 37

Simple example: Repetition (voting, D=3)• Repetition code (1-bit):

• Positives: simple

• Negatives: – Expensive: only 33% of code word is data

– Not packed in Hamming-bound sense (only D=3). Could get much more efficient coding by encoding multiple bits at a time

101

011H

1

1

1

G

C0

C1

C2

Error

v0

C0

C1

C2

3/30/2009 cs252-S09, Lecture 16 38

p1

p2

p3

Note: number bits from left to right.

• Example: (7,4) code: – Protect 4 data bits with 3 parity bits

1 2 3 4 5 6 7

p1 p2 d1 p3 d2 d3 d4

• Bit position number

001 = 110

011 = 310

101 = 510

111 = 710

010 = 210

011 = 310

110 = 610

111 = 710

100 = 410

101 = 510

110 = 610

111 = 710

Simple Example: Hamming Code (d=3)

1111000

1100110

1010101

H

1000

0100

0010

1110

0001

1101

1011

G

3/30/2009 cs252-S09, Lecture 16 39

How to correct errors?• But – what is the distance of the code? Not obvious

• Instead, consider a parity-check matrix H (n[n-k])

– Compute the following syndrome Si given code element Ci:

• Suppose that two correctable error vectors E1 and E2 produce same syndrome:

• But, since both E1 and E2 have (d-1)/2 bits, E1 + E2 d-1 bits set this cannot be true!

• So, syndrome is unique indicator of correctable error vectors

ECS ii HH

set bits moreor d has

0

21

2121

EE

EEEE

HHH

3/30/2009 cs252-S09, Lecture 16 40

Example, d=4 code (SEC-DED)• Design H with:

– All columns non-zero, odd-weight, distinct» Note that odd-weight refers to Hamming Weight, i.e. number of zeros

• Why does this generate d=4?– Any single bit error will generate a distinct, non-zero value– Any double error will generate a distinct, non-zero value

» Why? Add together two distinct columns, get distinct result– Any triple error will generate a non-zero value

» Why? Add together three odd-weight values, get an odd-weight value– So: need four errors before indistinguishable from code word

• Because d=4:– Can correct 1 error (Single Error Correction, i.e. SEC)– Can detect 2 errors (Double Error Detection, i.e. DED)

• Example:– Note: log size of nullspace will

be (columns – rank) = 4, so:» Rank = 4, since rows

independent, 4 cols indpt» Clearly, 8 bits in code word» Thus: (8,4) code

7

6

5

4

3

2

1

0

3

2

1

0

10001110

01001101

00101011

00010111

C

C

C

C

C

C

C

C

S

S

S

S

3/30/2009 cs252-S09, Lecture 16 41

Tweeks:• No reason cannot make code shorter than required

• Suppose n-k=8 bits of parity. What is max code size (n) for d=4?

– Maximum number of unique, odd-weight columns: 27 = 128

– So, n = 128. But, then k = n – (n – k) = 120. Weird!

– Just throw out columns of high weight and make 72, 64 code!

• But – shortened codes like this might have d > 4 in some special directions

– Example: Kaneda paper, catches failures of groups of 4 bits

– Good for catching chip failures when DRAM has groups of 4 bits

• What about EVENODD code?– Can be used to handle two erasures

– What about two dead DRAMs? Yes, if you can really know they are dead

3/30/2009 cs252-S09, Lecture 16 42

3/30/2009 cs252-S09, Lecture 16 43

Aside: Galois Field Elements• Definition: Field: a complete group of elements with:

– Addition, subtraction, multiplication, division– Completely closed under these operations– Every element has an additive inverse– Every element except zero has a multiplicative inverse

• Examples:– Real numbers– Binary, called GF(2) Galois Field with base 2

» Values 0, 1. Addition/subtraction: use xor. Multiplicative inverse of 1 is 1– Prime field, GF(p) Galois Field with base p

» Values 0 … p-1» Addition/subtraction/multiplication: modulo p» Multiplicative Inverse: every value except 0 has inverse» Example: GF(5): 11 1 mod 5, 23 1mod 5, 44 1 mod 5

– General Galois Field: GF(pm) base p (prime!), dimension m» Values are vectors of elements of GF(p) of dimension m» Add/subtract: vector addition/subtraction» Multiply/divide: more complex» Just like read numbers but finite!» Common for computer algorithms: GF(2m)

3/30/2009 cs252-S09, Lecture 16 44

Reed-Solomon Codes• Galois field codes: code words consist of symbols

– Rather than bits

• Reed-Solomon codes:– Based on polynomials in GF(2k) (I.e. k-bit symbols)– Data as coefficients, code space as values of polynomial:– P(x)=a0+a1x1+… ak-1xk-1

– Coded: P(0),P(1),P(2)….,P(n-1)– Can recover polynomial as long as get any k of n

• Properties: can choose number of check symbols– Reed-Solomon codes are “maximum distance separable” (MDS)– Can add d symbols for distance d+1 code– Often used in “erasure code” mode: as long as no more than n-k

coded symbols erased, can recover data

• Side note: Multiplication by constant in GF(2k) can be represented by kk matrix: ax

– Decompose unknown vector into k bits: x=x0+2x1+…+2k-1xk-1

– Each column is result of multiplying a by 2i

3/30/2009 cs252-S09, Lecture 16 45

Reed-Solomon Codes (con’t)

4

3

2

1

0

43210

43210

43210

43210

43210

43210

43210

77777

66666

55555

44444

33333

22222

11111

a

a

a

a

a

G

• Reed-solomon codes (Non-systematic):

– Data as coefficients, code space as values of polynomial:

– P(x)=a0+a1x1+… a6x6

– Coded: P(0),P(1),P(2)….,P(6)

• Called Vandermonde Matrix: maximum rank

• Different representation(This H’ and G not related)

– Clear that all combinations oftwo or less columns independent d=3

– Very easy to pick whatever d you happen to want

• Fast, Systematic version of Reed-Solomon:

– Cauchy Reed-Solomon

1111111

0000000'

7654321

7654321H

3/30/2009 cs252-S09, Lecture 16 46

Conclusion• Main memory is Dense, Slow

– Cycle time > Access time!

• Techniques to optimize memory– Wider Memory

– Interleaved Memory: for sequential or independent accesses

– Avoiding bank conflicts: SW & HW

– DRAM specific optimizations: page mode & Specialty DRAM

• ECC: add redundancy to correct for errors– (n,k,d) n code bits, k data bits, distance d

– Linear codes: code vectors computed by linear transformation

• Erasure code: after identifying “erasures”, can correct

• Reed-Solomon codes – Based on GF(pn), often GF(2n)

– Easy to get distance d+1 code with d extra symbols

– Often used in erasure mode