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Signal processing with the MAXQ multiply-accumulate unit (MAC)

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Signal processing with the MAXQ

multiply-accumulate unit (MAC)

Traditional microcontrollers and digital signal processors (DSPs) are sometimes viewed as standing

at opposite ends of the microcomputer spectrum. While microcontrollers are best suited for control

applications that require low-latency response to unsynchronized events, DSPs shine in applications

where intense mathematical calculations are required. A microcontroller can be used in heavy

arithmetic applications, but the one-operation-at-a-time nature of most microcontroller ALUs makes

such use less than optimal. Similarly, a DSP can be forced into a control application, but the internal

architecture of most DSPs render this operation inefficient in both code and time.

Choosing a DSP or a traditional microcontroller becomes more difficult when a mostly control-

oriented application requires a small amount of signal processing. In such applications, it is tempting

to squeeze the DSP code into the microcontroller. However, the designer often finds that the

application spends most time performing DSP functions, thus making the control application suffer.

This dichotomy can be resolved in modern processor architectures, such as the MAXQ

architecture. In the modular MAXQ architecture, a multiply-accumulate unit (MAC) can be

added to the design and integrated into the architecture with ease. With the hardware MAC,

16 x 16 multiply-accumulate operations occur in one cycle without compromising the

application running on the control processor. This article provides some examples of how the

MAC module in a typical MAXQ microcontroller can be used to solve such real-world problems.

Using the MAC module with a MAXQ

A common application for DSPs is filtering some analog signal. In this application, a properly

conditioned analog signal is presented to an ADC, and the resulting stream of samples is filtered in

the digital domain. A general filter implementation can be realized by the following equation:

y[n] = bix[n-i] + aiy[n-i]

where bi and ai characterize the feedforward and feedback response of the system, respectively.

Depending on the values of ai and bi, digital filters can be classified into two broad categories:

finite impulse response (FIR) and infinite impulse response (IIR). When a system does not

contain any feedback elements (all ai = 0), the filter is said to be of the FIR type:

y[n] = bix[n-i]

However, when elements of both ai and bi are non-zero, the system is an IIR filter.

As can be seen from the above equation for an FIR filter, the main mathematical operation is to

multiply each input sample by a constant, and then accumulate each of the products over the n

values. The following C fragment illustrates this:


for(i=0; iy[n] += x[i] * b[i];

For a microprocessor with a multiplier unit, this can be achieved according to the following

pseudo-assembler code:

move ptr0, #x ;Primary data pointer -> samples

move ptr1, #b ;Secondary DP -> coefficients

move ctr, #n ;Loop counter gets number of samples

move result, #0 ;Clear result register

In the modular MAXQ

architecture, a single-

cycle multiply-

accumulate (MAC) unit is

incorporated to facilitate

operation required for a

typical signal-processing





move acc, @ptr0 ;Get a sample

mul @ptr1 ;Multiply by coefficient

add result ;Add to previous result

move result, acc ;...and save the result back

inc ptr0 ;Point to next sample

inc ptr1 ;Point to next coefficient

dec ctr ;Decrement loop counter

jump nz, ACC_LOOP ;Jump if there are more samples


Thus, even with a multiplier, the multiply and accumulate loop requires 12 instructions and

(assuming a one-cycle execution unit and multiplier) 4 + 8n cycles.

The MAXQ multiplier is a true multiply-accumulate unit. Performing the same operation in the

MAXQ architecture shrinks code space from 12 words to 9 words, and execution time is reduced

to 4 + 5n cycles.

move DP[0], #x ; DP[0] -> x[0]

move DP[1], #b ; DP[1] -> b[0]

move LC[0], #loop_cnt ; LC[0] -> number of samples

move MCNT, #INIT_MAC ; Initialize MAC unit


move DP[0], DP[0] ; Activate DP[0]

move MA, @DP[0]++ ; Get sample into MAC

move DP[1], DP[1] ; Activate DP[1]

move MB, @DP[1]++ ; Get coeff into MAC and multiply

djnz LC[0], MAC_LOOP

Note that in the MAXQ multiply-accumulate unit, the requested operation occurs automatically

when the second operand is loaded into the unit. The result is stored in the MC register. Note

also that the MC register is 40 bits long, and thus can accumulate a large number of 32-bit

multiply results before overflow. This improves on the traditional approach where overflow

must be tested after every atomic operation. To illustrate how the MAC can be used efficiently

in the signal-processing flow, we present a simple application for a dual-tone multi-frequency

(DTMF) transceiver.

DTMF overview

DTMF is a signaling technique used in the telephone network to convey address information

from a network terminal (a telephone or other device) to a switch. The mechanism uses two sets

of four discrete tones that are not harmonically related, i.e., the "low group" (less than 1kHz) and

the "high group" (greater than 1kHz). Each digit on the telephone keypad is represented by

exactly one tone from the low group and one tone from the high group. See Figure 1 to learn

how the tones are allocated.

DTMF tone encoder

The encoder portion of the DTMF transceiver is relatively straightforward. Two digital sine-

wave oscillators are required, each of which can be tuned to one of the four low-group or high-

group frequencies.

There are several ways to resolve the issue of digitally synthesizing a sine wave. One method of

sine-wave generation avoids the issue of digital synthesis altogether. Instead, it just strongly filters

a square wave produced on a port pin. While this method works in many applications, Bellcore

requirements dictate that the spectral purity of the sine waves be higher than can be achieved using

this technique.

The dual-tone multi-

frequency (DTMF)

signaling technique used

in the telephone

network conveys

address information

from a network terminal

(telephone or other

device) to a switch. the MAXQ multiply-

accumulate unit, the

requested operation

occurs automatically

when the second

operand is loaded into

the unit.


A second method of generating sinusoidal waveforms is the table-lookup

method. In this method, one-quarter of a sine wave is stored in a ROM table, and

the table is sampled at a precomputed interval to create the desired waveform.

Creating a quarter-sine table of sufficiently high resolution to meet spectral

requirements would, however, require a significant amount of storage.

Fortunately, there is a better way.

A recursive digital resonator1

can be used to generate the sinusoids (Figure 2).

The resonator is implemented as a two-pole filter described by the following

difference equation:

Xn = k * Xn-1 - Xn-2

where k is a constant defined as

k = 2 cos(2 * toneFrequency / samplingRate)

Because only a small number of tones are needed in a DTMF dialer,

the eight values of k can be precomputed and stored in ROM. For

example, the constant required to produce a Column 1 tone (770Hz) at

a sample rate of 8kHz is:

k = 2 cos(2 * 770 / 8000) = 2 cos(0.60) = 1.65

One more value must be calculated: the initial impulse required to make the

oscillator begin running. Clearly, if Xn-1 and Xn-2 are both zero, every

succeeding Xn will be zero. To start the oscillator, set Xn-1 to zero and set

Xn-2 to

Xn-2 = -A * sin(2 * toneFrequency / samplingRate)

In our example, assuming a unit sine wave is desired, this reduces to:

Xn-2 = -1 * sin(2 * 770 / 8000) = -sin(0.60) = -0.57

Reducing this to code is simple: first, two intermediate variables (X1, X2) are

initialized. X1 is initialized to zero, while X2 is loaded with the initial

excitation value (calculated above) to start the oscillation. To generate one

sample of the sinusoid, perform the following operation:

X0 = k * X1 - X2

X2 = X1

X1 = X0

Each new sine value is calculated using one multiplication and one subtraction. With a single-cycle

hardware MAC on the MAXQ microcontroller, the sine wave can be generated as follows:

move DP[0], #X1 ; DP[0] -> X1

move MCNT, #INIT_MAC ; Initialize MAC unit

move MA, #k ; MA = k

move MB, @DP[0]++ ; MB = X1, MC=k*X1, point to X2

move MA, #-1 ; MA = -1

move MB, @DP[0]-- ; MB = X2, MC=k*X1-X2, point to X1

nop ; wait for result

move @--DP[0], MC ; Store result at X0

The MAXQ microcon-

troller, together with its

MAC unit, is bridging

the gap between the

traditional microcon-

troller and the digital

signal processor.

1 2 3 A

4 5 6 B

7 8 9 C

* 0 # D

1209Hz 1633Hz1477Hz1336Hz


















0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0


Figure 1. Combining one

frequency from the high-

frequency group and one

from the low-frequency

group generates a DTMF


Figure 2. A recursive

resonator generates the

sine wave.


DTMF tone detection

Because only a small number of frequencies are to be detected, the modified

Goertzel algorithm2

is used. This algorithm is more efficient than the general

DFT mechanisms and provides reliable detection of inband signals. It can be

implemented as a simple second-order filter following the format in Figure 3.

To use the Goertzel algorithm to detect a tone of a particular frequency, a

constant must first be precomputed. For a DTMF detector, this can be done at

compile time. All the tone frequencies are well specified. The constant is

computed from the following formula:

k = toneFrequency / samplingRate

a1 = 2cos(2k)

First, three intermediate variables (D0, D1, and D2) are initialized to zero. Now,

for each sample X received, perform the following:

D0 = X + a1 * D1 - D2

D2 = D1

D1 = D0

After a sufficient number of samples has been received (usually 205 if the

sample rate is 8kHz), compute the following using the latest computed values

of D1 and D2:

P = D12

+ D22

- a1 * D1 * D2

P now contains a measure of the squared power of the test frequency in the input

signal. To decode full four-column DTMF, each sample will be processed by

eight filters. Each filter will have its own k value, and its own set of intermediate

variables. Since each variable is 16 bits, the entire algorithm will require 48

bytes of intermediate storage.

Once the P values for various tone frequencies are calculated, one tone in the

high and low groups will have values significantly higher than all the other

tones, which means more than twice as high, often more than an order of

magnitude. Figure 4 shows a sample input signal to the decoder, and Figure 5

illustrates the result of the Goertzel algorithm. If the signal spectrum does not

meet this criterion, it either means that no DTMF energy is present in the signal,

or that there is sufficient noise to block the signal.

A spreadsheet that demonstrates this algorithm is available on our website, as

well as sample code for the MAC-equipped MAXQ processor. Go to


The MAXQ microcontroller, together with its MAC, is bridging the gap between

the traditional microcontroller and the digital signal processor. With the addition of

a hardware MAC, the MAXQ microcontroller offers a new level of signal-

processing capability to the 16-bit microcontroller market not previously available.

Real-time signal processing is made possible with a single-cycle MAC that

provides the functions most often required in real-world applications.

1 Todd Hodes, John Hauser, Adrian Freed, John Wawrzynek, and David Wessel. Proceedings of the IEEE International Conference on

Acoustics, Speech, and Signal Processing (ICASSP-99, March 15-19, 1999), pp. 993-996.

2 Alan Oppenheim and Ronald Schafer, Discrete-Time Signal Processing. Prentice Hall.










+ +







0 20 40 60 80 100 120 140 160 180 200

Figure 3. The Goertzel

algorithm is implemented

as a second-order filter.










697 770 852 941 1209


1336 1477 1633

Figure 4. This is the

sample input waveform for

the DTMF decoder.

Figure 5. The DTMF

decoder detects the

magnitude of various


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