Modular Exponent realization on FPGAs

Modular Exponent realization on FPGAs
Modular Exponent realization on FPGAs
Jüri Põldre | Kalle Tammemäe | Marek Mandre
Tallinn Technical University
Computer Engineering Department
jp@pld.ttu.ee
Abstract. The article describes modular exponent calculations used widely in
cryptographic key exchange protocols. The measures for hardware consumption
and execution speed based on argument bit width and algorithm rank are
created. The partitioning of calculations is analyzed with respect to interconnect
signal numbers and added delay. The partitioned blocks are used for
implementation approximations of two different multiplier architectures.
Examples are provided for 3 families of FPGAs: XC4000, XC6200 and
FLEX10k
1 Introduction
Modular exponent calculations are widely used in Secure Electronic Trading (SET)
protocols for purchasing goods over Internet. One transaction in SET protocol
requires the calculation of six full-length exponents. Because of advances in factoring
and ever-increasing computing power the exponent size has to be at least 1024 bits
now and predicted 2048 in 2005 to guarantee the security [1].
The calculations with very large integers are managed in software by breaking
them down to the host processor word size. The exponent is usually calculated by
progressive squaring method and takes 2×N modular multiplications to complete. One
modular multiply takes at least (A/W)×(N×N) instructions to complete, where N is
argument bit length, W is host processor word size and A>1 is a constant depending
on the algorithm used. As exponent calculation demands N multiplications that leaves
3
us with N complexity.
As of now the need for SET transactions is about one per second. It is estimated to
reach over 200 per second in servers after 18 months. Today the common PC Pentium
200 MHz processors can calculate one exponent in 60 msec. Taking 6 exponents per
SET transaction we have 100% load in 2¾ SET transactions per second.
As the calculations are very specific it is not likely that they will become a part of
general-purpose processors, although internal study by Intel Corporation has been
carried out to find possible instruction set expansion for cryptographic applications in
1997. Sadly only abstract of that is available for public review. A separate unit in
system for cryptographic calculations also increases security by creating “secure area”
for sensitive information.
Recently many companies have come up with the product to solve this problem.
Usually these consist of RISC processor core, flash ROM, RAM and exponent
accelerator unit. Several products on the market are ranging from Rainbow
CryptoSwift with 54 msec to Ncipher Nfast 3 msec per exponent. They also have
different physical interfaces – Cryptoswift uses PCI bus and Ncipher is a SCSI
device. Cryptoswift uses ARM RISC processors and Ncipher ASICs. Several other
designs have been created including authors IDEA/RSA processor [7]. Prices for
these products range from $1600-$3000 per device.
Standards are being developed and new algorithms proposed, so the design lifespan
of these accelerators is quite low. A solution here is to use a cryptography system
library with certain functions being accelerated in reconfigurable hardware – FPGA.
FPGA based accelerator board supplied with PCI-interface is universal device,
which can be plugged into any contemporary PC for accelerating RSA-key encodingdecoding task. Considering the fact, that Sun has already included PCI-interface into
Ultrasparc workstation configuration, the board suits there as well, reducing
computation load of main processor(s).
In the course of this work we will look into accelerating exponent calculations
using two different methods. Both of them use progressive squaring, Montgomery
reduction, and redundantly represented partial product accumulation. The difference
is in the architecture of modular multiplier unit.
In following pages we:
• Select the appropriate exponentiation (multiplication) algorithm.
• Define the hardware building blocks for the algorithm.
• Analyze two different architectural approaches using the blocks from previous
stage.
In every step the alternative approaches and reasoning behind selection are
presented.
2 Modular exponentiation
will be handled by right-left binary method. It gives the possibility to run two
multiplications per iteration in parallel and thus half the execution time if sufficient
hardware resources are available. It can also be utilized easily for interleaved
calculations as two arguments (N, P) are same for both multiplications
e
To find C := M mod N proceed as follows:
Input:
Output:
Temporary variable:
Exponent size:
th
i bit of e:
base M; exponent e; moduli N;
e
C := M mod N
P
h
ei
2
Algorithm:
1. C := 1; P := M
2.
for i = 0 to h - 2
2a.
if ei = 1 then C := C × P (mod N)
2b.
P := P × P (mod N)
3. if eh -1 = 1 then C := C × P (mod N)
4. return C
Further possibilities for reducing number of multiplications are not considered in this
work. These methods involve precalculated tables and short exponents [6]. The
support for precalculated tables can be added at higher level of hierarchy using host
processor. For the course of the article let the time for exponentiation be equal to
number of bits in the exponent:
Texp=h × Tmult
h:
Texp:
Tmult:
(1)
number of bits in exponent,
time for exponent calculation,
time for multiplication.
3 Modular multiplication
is the only operation in exponentiation loop. The modular multiplication is
multiplication followed by dividing the result by moduli and returning quotient:
C = A × B mod N
(2)
T=A×B
Q=T/N
C=T–Q×N
We can calculate the multiplication and then divide by moduli, but these operations
can also be interleaved. This reduces the length of operands and thus hardware
consumption. The algorithms rank k is the amount of bits handled at one step.
The main updating line in interleaved k-ary modular multiply algorithm is:
Si+1 = Si << k + A × Bi – QI× N
(3)
Not going any further into details [2] let us point out that most time-consuming
operation is to find Qi, what is Si-1 / N. Several approaches have been proposed. All
of them use approximation to find Qi. Some of them involve multiplication, others
table lookup. All of them consume silicon resources, but mainly they increase cycle
time significantly.
3
In 1985 Montgomery [3] proposed new method for solving the problem of quotient
digit calculation. After some preprocessing it is possible to calculate the loop updating
line as:
S i+1 = Si >> k + Ã × Bi + QI × Ñ
(4)
Qi is equal to k least significant bits of partial sum Si. This comes at a price of
transforming the initial arguments and moduli to Montgomery residue system and the
result back. The transformations can be carried out using the same Montgomery
(2 × h)
mod N is
modular multiplication operation (arguments conversion constant 2
k
needed, but it can easily be calculated in software). Another restriction is that 2 and
N should be relatively prime. As application area is cryptography even N will never
occur and thus this condition is satisfied.
The exponentiation process now takes two more multiplications to complete for
transforming arguments. Because argument length h is usually large (more than 1024
bits) it is ca two tenths of percent. This introduced delay is negligible taken into
account the cycle speedup and hardware savings.
4 Hardware blocks
To describe hardware architectures the main building blocks for them are needed.
Montgomery multiplication (4) needs multiplication, summation and shift operators.
Because the argument sizes are large and result is not needed in normal form before
the end of the exponentiation it is reasonable to use redundant representation of
arguments - 2 digits to represent one. The exact value of the digit is the sum of these
two components.
Double amount of hardware is needed for handling redundant numbers, but it
postpones carry ripple time until the result is needed in normal form. Even if we
would construct a fast adder (CLA) it spends more than twice hardware and definitely
has larger power consumption.
Shifting these digits is straightforward and involves shifting both numbers.
For addition normal full-adder cells can be used. Two such cells forms an adder for
redundant numbers what is called 4-2 adder (Add4-2):
4
Ci1
A0
A1
A2
A3
Ci2
a
s
Full
b
Adder c
c
a
Full s
b
Adder c
c
Co1
O0
O1
Co2
Fig. 1. 4-2 adder from two full adders
Connecting carries ci • co of h such blocks generates h-bit redundant adder. As it
can be seen from the figure the maximal delay for such addition does not depend on
argument length and equals to two full-adder cell delays.
Before making multiplier we will look at multiplicand recording. If both positive
and negative numbers are allowed in multiplication partial product accumulation, then
the number of terms can be halved. This process is called Booth encoding. Following
table should illustrate the idea:
Table 1. Booth recording of 3-bit multiplicand
B
Term1
Term2
0
1
2
3
4
5
6
7
-0
-1
-2
-1
-0
-1
-2
-1
4×0
4×0
4×0
4×1
4×1
4×1
4×1
4×2
Calculating multiple of A×B in usual way requires 3 terms: A , A<<1, A<<2. By
recording B differently we can do away with only two. The multiplication constants
k
for terms are 0,1,2 and –1. Multiplications by 2,4,2 can be handled with shift.
Negation uses complementary code: –A = (not A) +1. Carry inputs to partial sum
accumulation tree structure are utilized for supplying additional carries.
One term of partial sum is thrown away by adding 4-input multiplexers (Mux4).
The same method can be used to make 5 → 3, 7 → 4, … etc. encodings. Generally
we will have:
(N-1) → N/2
5
(5)
Booth recorder is required for generating multiplexer control information from
multiplier bits. As this circuit is small and only one is needed for multiplier we will
not look into that more deeply.
The multiplier consists of Booth encoder, shifter and redundant adder tree. It has
redundantly represented base N, Booth recorded multiplicand B and redundant result
O. The terms from Booth encoders will be accumulated using tree of 4-2 adders.
Carry inputs and outputs are for expansion purposes and for negation control at LSB
end of digit.
Carry in
B
Ns
Nc
Mult
B*N
Os
Oc
Carry out
Fig. 2. B×N multiplier
The total component delay of this circuit is the sum of multiplexer delay and delay
introduced by 4-2 adder tree. This delay is proportional to tree depth or log2 of size of
B in digits. We can write delay as following:
Tmult = Tmux4 + log2( size(B) ) × Tadd4-2
(6)
Tmult
Time for multiplication.
Mux4 cell delay.
Tmux4
Add4-2 cell delay.
Tadd4-2
Size(B)
size of Booth recorded number in digits.
The number of elements required for building such block is:
CountMux4 = N × 2 × size(B)
(7)
CountAdd4-2 = N × 2 × (log2(size(B)) – 1 ) + 1
(8)
Here is an example to clarify the formulas:
Device is 4 booth digits × 8 bits or 7 × 8 bit multiplier. The result is calculated in
Tmux4 + 2 × Tadd4-2 time units. The number of 4 - input multiplexers is 8 × 2 × 4 and
the count of 4 - 2 adders is 16 + 1. The multiplier structure for one output bit is
described in figure below.
6
B
Ns
4
Booth
encoders
4-2 adder
Os
4-2 adder
Nc
4
Booth
encoders
Oc
4-2 adder
Fig. 3. 7×8 multiplier structure
Each 4-2 adder generates two carries. Booth encoder needs higher bits of previous
operand to generate terms, adding two times the size of B carries for both input
operands. The total is thus 3 × 2 + 4 × 2 + 2 = 16 carries in and 16 carries out. The
formula for counting carry signal numbers is:
(log2(size(b)) + size(b) + 1) × 2
(9)
5 FPGA resources
For FPGA realizations the resource allocation formulas for these operators are
needed. We will consider 3 series: Xilinx 4000, Xilinx 6200 and Altera FLEX10K.
As all previously described blocks contain simple routing what is connected only to
closest neighbors we will ignore the routing expenses in calculations and concentrate
in CLB count.
The above described two operators demand the following hardware resources:
• MUX4 (may be built from two 2MUX cells).
• ADD4-2 (consists of two full-adder cells).
The following table will sum the resources needed to build these blocks in each of
mentioned families:
7
Table 2. Cell hardware requirements
Cell name
MUX4
ADD4-2
XC4000
XC6200
FLEX10K
1
1∗
2
6
2
2
∗ Actually it is 2 ADD4-2 cells per 2 blocks, because one block implements 2-bit full adder.
6 Architectural solutions
Having the blocks let us now consider different architectures for implementation.
6.1 Traditional k-ary algorithm
Calculates the Montgomery multiplication by directly implementing the loop updating
statement:
Br
Z
k bit full adder
k*h
mux k bits
S42
P
C
A
S42
shr k
k*h
Acu
Fig. 4.Traditional architecture for calculating Montgomery multiplication
Z, A, ACU are h bit registers. k×h multiplier forming BI × A is the multiplier cell
described above. Qi × Z is the same cell with only half of hardware, because Z is in
normal (non-redundant) representation. Two 4-2 adders (S42) accumulate the results.
By adding registers in accumulator structure it is possible to interleave two
multiplications as described earlier. Control unit must multiplex Bi each clock tick
from P or C. At the end of calculations C and P are updated. The updating of P is
th
done conditionally depending on value of ei, the i bit of exponent.
6.2 K-ary systolic architecture
This approach uses array of identical blocks. Each block calculates digit(s) of result.
By connecting these blocks it is possible to generate a hardware structure of arbitrary
8
bitlength. Remarkable features of this approach are expandability and relative ease of
construction. Once you have mastered the primitive block it is easy to place them on
CLB array or silicon. The structure of systolic array multiplier consisting of h / (2 × k)
cells implementing (4) is:
A
...
...
0
ModMul
CELL
ModMul
CELL
...
...
Bi
Bi+1
ModMul
CELL
Si
Qi+1
Br
Si+1
Qi
Bi
k-bit adder
Br
...
N
Fig. 5. Systolic multiplier structure
In each cell two terms of partial sum are calculated and summed [5]. Sum term is
represented redundantly, but k-bit full adder converts it back to normal form.
Therefore cell contains four k × k non-redundant input multipliers and accumulator
tree of 4-2 adders (Fig 6). B, Q and S are k bit registers. Cell also contains carry
memory what adds twice the number of S42 block count registers to cell memory
requirements.
Ai+1
Bi+1
Ai
B
k*k
k*k
S42
S42
k*k
Qi+1
k*k
Qi
Q
Ni+1
Si
A
Bi
Ni
S42
S42
S
Si+1
N
Fig. 6.. One systolic array cell
7 Analysis of implementations
Both the systolic and classical solution calculate the statement (2) with the same
delay. As systolic solution is accumulating 2 terms it ads one S42 delay. Thus the
formulas for calculating cycle length are (calculated in 4-2 adder delays):
9
Tclassic =log2( k/2 ) + 1
(10)
Tsystol =log2( k/2 ) + 2
(11)
We can further decrease time by adding registers at S42 outputs and using quotient
pipeline [4]. This reduces cycle delay to one S42 cell delay. It can be reduced further,
but registers in ASIC are expensive. This is not the case with FPGAs because the ratio
of register/logic is high and flip-flops are already there.
Systolic array is made of h / (2 × k) cells and each cell consists of four k × k
multipliers. Comparing that to standard approach with 1½ k × h multipliers:
4 × ( k × k × ½ ) × H/ ( k × 2 ) = 2 × ½ × k × h = k × h
(12)
In systolic array we have the result of multiplication in normal format, therefore we
need 1/3 less hardware. The following table sums the hardware consumption for both
architectures for 3 different algorithm ranks (k)
Table. 3. Hardware (CLB count) requirements for exponent calculator
ELWV
N ;& ;& ;& ;& )/(;. )/(;. F\FOHV F\FOHV
V\VWRO FODVVLF
V\VWRO
FODVVLF
V\VWRO
FODVVLF V\VWRO FODVVLF
The systolic structure calculates result in 2 × N / k steps. For exponent calculations
it is possible to either use twice the hardware or run two multiplications sequentially.
In the table above hardware consumption for single multiplication is provided.
Cycle speed is increased by having to partition the design on several FPGAs, for
large exponents do not fit into single FPGA. This additional delay consists of
CLB→IOB→PCB→IOB→CLB path. Each component adds it’s own delay. We will
use the 20 Ns safe figure here for this entire path. Thus the cycle times for chosen
families are:
T4000 = 20 + (1 + log2(k)) × 5
(13)
T6200= 20 + (2 + 3 × log2(k)) × 4
TFLEX10K= 20 + (2 +2 × log2(k)) × 5
First term is communication delay, then 4mux delay for Booth encoder and finally
logarithmic component for accumulator. The numbers behind parenthesis is CLB
delay added to closest neighbor routing of fastest member in the family. These are
optimistic values, but as structure is regular and routing is between the closest
10
neighbors the expected results should not differ from calculated more than 10%. The
values in the following table are exponent calculation times in msec.
Table 4. Exponent calculation timing
ELWV
N ;&
;&
;&
;&
)/(;.
)/(;.
F\FOHV
F\FOHV
V\VWRO
FODVVLF
V\VWRO
FODVVLF
V\VWRO
FODVVLF
V\VWRO
FODVVLF
For partitioning the largest circuits from each family were used. These are at the
current moment:
• XC6264 (16384 CLBs).
• XC4025 (1024 CLBs).
• EPF10K100 ( 4992 CLBs ).
Utilizing them the following number of chips is needed for implementation (table
5). To compare the speed-up of calculations the data from RSA Inc. Bsafe
cryptographic library is in table 6.
Table 5. Number of ICs for implementation
ELWV
N ;& ;&
V\VWRO FODVVLF
;&
V\VWRO
;& )/(;. )/(;.
FODVVLF
V\VWRO
FODVVLF
Table 6. Bsafe cryptolibrary execution benchmarks in seconds
Operand
length in bits
768
1024
Intel
Pentium
90 MHz
0.066
.140
Power
Macintosh
80 MHz
.220
.534
11
Sun
SparcStation
4 110 MHz
.212
.461
Digital
AlphaStation
255 MHz
0.024
0.043
8 Conclusions
In this paper we have analyzed the implementation of modular exponent calculator on
FPGAs. The appropriate algorithm for exponentiation and multiplication has been
selected. Realizations on three families of FPGAs were considered.
While two XC6216 circuits would nicely fit onto PCI board and give over 10 times
acceleration of calculations we must bear in mind that these circuits are quite
expensive.
Maybe simpler approach would help? If we use 1-bit-at-a-time algorithm we can fit
1024 bit calculator into one package. k=1 classic structure demands two 4-2 adders
per bit and requires H steps to complete. 1024 bit exponent is calculated with
1024×1024×2 cycles. As the structure is simpler the cycle delay can be decreased on
condition that we stay in limits of one package. That leaves us with 512 bit for XC4K,
1024 bit for XC6264 and 2500 for FLEX10K. The clock frequency can now be lifted
up to one CLB delay plus routing between closest neighbors. This can be as high 100
MHz calculating one exponent in 20 msec. This is comparable with Digital 255 MHz
processor. This is approximately 50 Kgates of accelerator hardware running at twice
slower speed.
As to now programmable hardware is still too expensive to be included on
motherboards but these figure shows a clear tendency that the devices together with
hardware-software co-development system and downloadable modules will become a
part of functioning computer system in nearest future.
References
[1] Schneier, Bruce. “Applied Cryptography Second Edition: protocols, algorithms
and source code in C”, 1996, John Wiley and Sons, Inc.
[2] Ç. K. Koç, “RSA Hardware implementation”, RSA laboratories, 1995.
[3] Peter L. Montgomery. ”Modular multiplication without trial division”,
Mathematics of Computation, 44(170):519-521. April 1985.
[4] Holger Orup. “Simplifying Quotient Determination in High-Radix Modular
Multiplication”, Aarhus University, Denmark. 1995.
[5] Colin D. Walter. “Systolic Modular Multiplication” IEEE transactions on
Computers, C-42(3)376-378, March 1993.
[6] B.J.Phillips, N.Burgess. “Algorithms of Exponentiation of Long Integers – A
survey of Published Algorithms”, The University of Adelaide, May 1996.
[7] Jüri Pôldre, Ahto Buldas: “A VLSI implementation of RSA and IDEA encryption
engine”, Proceedings of NORCHIP’97 conference. November 1997.
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