Digital Circuits/CORDIC
A CORDIC (standing for COordinate Rotation DIgital Computer) circuit serves to compute several common mathematical functions, such as trigonometric, hyperbolic and exponential functions.
Contents
Application[edit]
A CORDIC uses only adders to compute the result, with the benefit that it can therefore be implemented using relatively basic hardware.
Methods such as power series or table lookups usually need multiplications to be performed. If a hardware multiplier is not available, a CORDIC is generally faster, but if a multiplier can be used, other methods may be faster.
CORDICs can also be implemented in many ways, including a singlestage iterative method, which requires very few gates when compared to multiplier circuits. Also, CORDICs can compute many functions with precisely the same hardware, so they are ideal for applications with an emphasis on reduction of cost (e.g. by reducing gate counts in FPGAs) over speed. An example of this priority is in pocket calculators, where CORDICs are very frequently used.
History[edit]
The CORDIC was invented in 1959 by J.E. Volder, in the aeroelectronics departments of Convair, and was designed for the B58 Hustler bomber's navigational computer to replace an analogue resolver, a device that computed trigonometric functions.
In 1971, J.S. Walther, at HewlettPackard, extended the method to calculate hyperbolic and exponential functions, logarithms, multiplications, divisions, and square roots.
General Concept[edit]
Consider the following rotations of vectors:


If we were to have a computationally efficient method of rotating a vector, we can directly evaluate sine, cosine and arctan functions. However, rotation by an arbitrary angle is nontrivial (you have to know the sine and cosines, which is precisely what we don't have). We use two methods to make it easier:
 Instead of performing rotations, we perform "pseudorotations", which are easier to compute.
 Construct the desired angle θ from a sum of special angles, α_{i}:
The diagram belows shows a rotation and pseudorotation of a vector of length R_{i} about an angle of a_{i} about the origin:
A rotation about the origin produces the following coordinates:
Recall the identity .
Our strategy will be to eliminate the factor of and somehow remove the multiplication by . A pseudorotation produces a vector with the same angle as the rotated vector, but with a different length. In fact, the pseudorotation changes the length to:
Thus we now have these coordinates following a pseudorotation:
The pseudorotation has succeeded in removing our lengthfactor, which would have required a costly division operation. However, the vector will grow by a factor of K over a sequence of n pseudorotations:
The coordinates following the n pseudorotations are then:
If the angles are always the same set, then K is fixed, and can be accounted for later. We choose these angle according to two criteria:
 We must also choose the angles so that any angle can be constructed from the sum of all them, with appropriate signs.
 We make all a power of 2, so that the multiplication can be performed by a simple logical shift of a binary number.
The tangent function has a monotonically increasing gradient on the interval [0, π/2], so the tangent of a given angle is always less than twice the tangent of half the angle. This means that if we make the angles , we can satify both criteria. Note that the tangent function is odd, which means that to pseudorotate the other way, you just subtract, rather than add, the tangent of the angle.
i  α_{i} = tan^{−1} (2^{−i})  

Degrees  Radians  
0  45.00  0.7854 
1  26.57  0.4636 
2  14.04  0.2450 
3  7.13  0.1244 
4  3.58  0.0624 
5  1.79  0.0312 
6  0.90  0.0160 
7  0.45  0.0080 
8  0.22  0.0040 
9  0.11  0.0020 
In step i of the process, we pseudorotate by , where is the direction (or sign) of the rotation, which will be chosen at each step to force the angle to converge to the desired final rotation. For example, consider an rotation of 28°:
Them more steps we take, the better the approximation that we can make by successive rotations. Thus, we have the following iterative coordinate calculation:
In order to achieve k bit of precision, k iterations are needed, because , converging as i increases.
Using CORDICs[edit]
CORDICs can be used to compute many functions. A CORDIC has three inputs, x_{0}, y_{0}, and z_{0}. Depending on the inputs to the CORDIC, various results can be produced at the outputs x_{n}, y_{n}, and z_{n}.
Using CORDIC in rotation mode[edit]


For convergence of θ_{n} to 0, choose .
If we start with x_{0} = 1/K and y_{0}=0, at the end of the process, we find x_{n}=cos z_{0} and y_{n}=sin z_{0}.
The domain of convergence is because 99.7° is the sum of all angles in the list.
Using CORDIC in vectoring mode[edit]


For convergence of y_{n} to 0, choose .
If we start with x_{0} = 1 and z_{0} = 0, we find z_{n}=tan^{−1}y_{0}
Implementations of CORDICs[edit]
Bitparallel, unrolled[edit]
Bitparallel, iterative[edit]
If a high speed is not required, this can be implemented with a single adder and a single shifter.
Bitserial[edit]
The Universal CORDIC[edit]
By introducing a factor μ, we can also cater for linear and hyperbolic functions:
Summary of Universal CORDIC implementations[edit]
Directly computable functions[edit]
Indirectly computable functions[edit]
In addition to the above functions, a number of other functions can be produced by combining the results of previous computations:
Further Reading[edit]
 Volder, J.E. (1959), " The CORDIC trigonometric computing technique", IRE Transactions on Electronic Computers 8 (3): 330–334, http://lapwww.epfl.ch/courses/comparith/Papers/3Volder_CORDIC.pdf, retrieved 20090602
 Walther, J.S. (1971), "A unified algorithm for elementary functions" (w), Proceedings of the May 1820, 1971, spring joint computer conference: 379385, http://portal.acm.org/citation.cfm?id=1478786.1478840, retrieved 20090602
 Parhami, B. (1999), Computer arithmetic: algorithms and hardware designs