This example shows how to compute sine and cosine using a CORDIC rotation kernel in MATLAB®. CORDIC-based algorithms are critical to many embedded applications, including motor controls, navigation, signal processing, and wireless communications.

Efficient MATLAB Implementation of a CORDIC Rotation Kernel Algorithm

CORDIC-Based Sine and Cosine Computation Using Normalized Inputs

CORDIC is an acronym for COordinate Rotation DIgital Computer. The Givens rotation-based CORDIC algorithm (see [1,2]) is one of the most hardware efficient algorithms because it only requires iterative shift-add operations. The CORDIC algorithm eliminates the need for explicit multipliers, and is suitable for calculating a variety of functions, such as sine, cosine, arcsine, arccosine, arctangent, vector magnitude, divide, square root, hyperbolic and logarithmic functions.

The fixed-point CORDIC algorithm requires the following operations:

1 table lookup

**per iteration**2 shifts

**per iteration**3 additions

**per iteration**

You can use a CORDIC rotation computing mode algorithm to calculate sine and cosine simultaneously, compute polar-to-cartesian conversions, and for other operations. In the rotation mode, the vector magnitude and an angle of rotation are known and the coordinate (X-Y) components are computed after rotation.

The CORDIC rotation mode algorithm begins by initializing an angle accumulator with the desired rotation angle. Next, the rotation decision at each CORDIC iteration is done in a way that decreases the magnitude of the residual angle accumulator. The rotation decision is based on the sign of the residual angle in the angle accumulator after each iteration.

In rotation mode, the CORDIC equations are:

where if , and otherwise;

, and is the total number of iterations.

This provides the following result as approaches :

Where:

.

Typically is chosen to be a large-enough constant value. Thus, may be pre-computed.

In rotation mode, the CORDIC algorithm is limited to rotation angles between and . To support angles outside of that range, quadrant correction is often used.

A MATLAB code implementation example of the CORDIC Rotation Kernel algorithm follows (for the case of scalar `x`

, `y`

, and `z`

). This same code can be used for both fixed-point and floating-point operation.

**CORDIC Rotation Kernel**

function [x, y, z] = cordic_rotation_kernel(x, y, z, inpLUT, n) % Perform CORDIC rotation kernel algorithm for N iterations. xtmp = x; ytmp = y; for idx = 1:n if z < 0 z(:) = accumpos(z, inpLUT(idx)); x(:) = accumpos(x, ytmp); y(:) = accumneg(y, xtmp); else z(:) = accumneg(z, inpLUT(idx)); x(:) = accumneg(x, ytmp); y(:) = accumpos(y, xtmp); end xtmp = bitsra(x, idx); % bit-shift-right for multiply by 2^(-idx) ytmp = bitsra(y, idx); % bit-shift-right for multiply by 2^(-idx) end

**Sine and Cosine Computation Using the CORDIC Rotation Kernel**

The judicious choice of initial values allows the CORDIC kernel rotation mode algorithm to directly compute both sine and cosine simultaneously.

First, the following initialization steps are performed:

The angle input look-up table

`inpLUT`

is set to`atan(2 .^ -(0:N-1))`

.is set to the input argument value.

is set to .

is set to zero.

After iterations, these initial values lead to the following outputs as approaches :

Other rotation-kernel-based function approximations are possible via pre- and post-processing and using other initial conditions (see [1,2]).

The CORDIC algorithm is usually run through a specified (constant) number of iterations since ending the CORDIC iterations early would break pipelined code, and the CORDIC gain would not be constant because would vary.

For very large values of , the CORDIC algorithm is guaranteed to converge, but not always monotonically. You can typically achieve greater accuracy by increasing the total number of iterations.

**Example**

Suppose that you have a rotation angle sensor (e.g. in a servo motor) that uses formatted integer values to represent measured angles of rotation. Also suppose that you have a 16-bit integer arithmetic unit that can perform add, subtract, shift, and memory operations. With such a device, you could implement the CORDIC rotation kernel to efficiently compute cosine and sine (equivalently, cartesian X and Y coordinates) from the sensor angle values, without the use of multiplies or large lookup tables.

sumWL = 16; % CORDIC sum word length thNorm = -1.0:(2^-8):1.0; % Normalized [-1.0, 1.0] angle values theta = fi(thNorm, 1, sumWL); % Fixed-point angle values (best precision) z_NT = numerictype(theta); % Data type for Z xyNT = numerictype(1, sumWL, sumWL-2); % Data type for X-Y x_out = fi(zeros(size(theta)), xyNT); % X array pre-allocation y_out = fi(zeros(size(theta)), xyNT); % Y array pre-allocation z_out = fi(zeros(size(theta)), z_NT); % Z array pre-allocation niters = 13; % Number of CORDIC iterations inpLUT = fi(atan(2 .^ (-((0:(niters-1))'))) .* (2/pi), z_NT); % Normalized AnGain = prod(sqrt(1+2.^(-2*(0:(niters-1))))); % CORDIC gain inv_An = 1 / AnGain; % 1/A_n inverse of CORDIC gain for idx = 1:length(theta) % CORDIC rotation kernel iterations [x_out(idx), y_out(idx), z_out(idx)] = ... fidemo.cordic_rotation_kernel(... fi(inv_An, xyNT), fi(0, xyNT), theta(idx), inpLUT, niters); end % Plot the CORDIC-approximated sine and cosine values figure; subplot(411); plot(thNorm, x_out); axis([-1 1 -1 1]); title('Normalized X Values from CORDIC Rotation Kernel Iterations'); subplot(412); thetaRadians = pi/2 .* thNorm; % real-world range [-pi/2 pi/2] angle values plot(thNorm, cos(thetaRadians) - double(x_out)); title('Error between MATLAB COS Reference Values and X Values'); subplot(413); plot(thNorm, y_out); axis([-1 1 -1 1]); title('Normalized Y Values from CORDIC Rotation Kernel Iterations'); subplot(414); plot(thNorm, sin(thetaRadians) - double(y_out)); title('Error between MATLAB SIN Reference Values and Y Values');

Jack E. Volder, The CORDIC Trigonometric Computing Technique, IRE Transactions on Electronic Computers, Volume EC-8, September 1959, pp330-334.

Ray Andraka, A survey of CORDIC algorithm for FPGA based computers, Proceedings of the 1998 ACM/SIGDA sixth international symposium on Field programmable gate arrays, Feb. 22-24, 1998, pp191-200

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