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FUZZY LOGIC IN EMBEDDED SYSTEMS
ABSTRACT:
A digitally  programmable analogue Fuzzy Logic Controller (FLC) is presented. Input and output signals are processed in the analog domain whereas the parameters of the controller are stored in a builtin digital memory. Some new functional blocks have been designed whereas others were improved towards the optimization of the power consumption, the speed and the
modularity while keeping a reasonable accuracy, as it is needed in several analogue signal processing applications. A ninerules, twoinputs and oneoutput prototype was fabricated and successfully tested using a standard CMOS 2.4 Technology, showing good agreement with the expected performances, namely: from 2.22 to 5.26 Mflips (Mega fuzzy logic inferences per second) at the pin terminals (@CL=13pF), 933Ã‚ÂµW power consumption per rule (@Vdd=5V) and 5 bits of resolution. Since the circuit is intended for a subsystem embedded in an application chip (@CL =5pF) up to 8 Mflips may be expected.
INTRODUCTION:
In the last years the application of Fuzzy Logic has been extended beyond the classical Process Control area where it has been employed from the beginning. Signal Processing, Image Processing, Power Electronics, seem to be others niches where this softcomputing technique can meet a broad range of applications. As real time processing mode need ever faster, more autonomous and less power consuming circuits the choice of onchip controllers become an interesting option. Digital Fuzzy Logic chips provide enough performance for general applications but their speed is limited, if compared with their analogue counterparts. Furthermore, in realtime applications digital fuzzy processors needs A/D and D/A converters to interface sensors and actuators, respectively.
On the other hand, pure analog processors suffer the lack of suppleness since full analog programmability is only feasible in special technologies allowing analog storage devices (i.e.: floating gate transistors). However, in the frame of standard CMOS technologies, a tradeoff between accuracy and flexibility is achieved when a finite discrete set of analogue parameters is provided. For instance, a voltage parameter can be settled by using a binaryscaled set of currents sources yielding a discrete set of voltage drops through a linear resistor. In such a case, it is possible to use a digital memory to store a given binary combination of the set of currents. This technique gives rise to the socalled MixedSignal analogue computation circuits [2]. It has been shown that analogue currentmode FLCs [2] lend themselves to simple rulesevaluation and aggregation circuits that can work at a reasonable speed. If some of the unwanted currenttovoltage and/or voltage to current intermediate converters can be avoided, the delay through cascaded operators may be even shortened and higher speeds achieved. This is interesting when Fuzzifiers [2, 3] and defuzzifiers [4] circuits are being designed for these circuits interact normally with a voltagemode controlled environment. On the other hand, to reduce die silicon area and power consumption some building blocks can be shared without altering functionality. As a result a relatively lowcomplexity layout can be obtained which leads to an additional gain of speed. In this work, a lowpower digitally programmable analogue Fuzzy Logic Controller (MixedSignal FLC) is introduced intended for embedded subsystems, as it is required for analogue signal processing applications of Mediumaccuracy (i.e.: nonlinear filtering [1], power electronics [9], etc). Keeping in mind the above exposed issues, new operators were designed while others were optimized achieving a flexible and high performance controller notwithstanding the limits imposed by the technology that was used for the demonstrator.
2. Architecture of the controller
Because it offers a good tradeoff between simplicity and accuracy, a zeroorder Sugeno architecture (consequents are singletons) was chosen. Figure 1 shows the block diagram of a twoinputs oneoutput controller, highlighting the three wellknown basic fuzzy operations (fuzzification, rulesevaluation and defuzzification) being performed concurrently. The MIN inference method may be also stated as: MIN (A, B,...) = 1  MAX (1A, 1B,Â¦). Therefore, for the general case of a qinputs, mrules controller, a set of Complementary Fuzzy Membership Functions (CFMF) per input, being shared by several rules, followed by m qinputs MAX operators performs the two first operations. After complementing the outputs of the MAX operators the firing degree of each rule is provided in the form of a current signal Ii. At the last stage, each Ii current is replicated (n+1)\ times via unit gain mirrors, where n stands for the resolution of the singleton discretevalue ai of the consequents of the rules. This last is codified accordingly with the state of the switches: Cn1Â¦Â¦C0. Finally a common currentmode Digital to Analogue converter (D/A), used as a weighting operator, together with an analogue divider takes care of the computation of the AveragedWeighted Sum (AWS), rendering the defuzzified output value Vo equal to:
Where k is a voltagedimension constant defined by the transfer function of the divider itself.
2.1. Complementary fuzzy membership functions
Lowpower fuzzy controllers need relatively low tranconductance values for their membership function circuits. Consequently, CMOS triode transconductors can be used to meet that requirement smartly. The circuit of the complementary fuzzifier is depicted in figure 2 a). It is composed by two almost linear regulatedcascode transconductors (ML1, ML3, DAL  MR1, MR3, DAR) each one controlling one edge of the CFMF whose shape is nearly an inverted trapezoid. Transistors ML2, MR2 have fixed large sizes, so that their gatevoltage voltage overdrive (VgsVTn) can be neglected. Reference voltages VKL, VKR define the knees where conduction begins falling towards zero or rising towards Io respectively in each transconductor. Slopes and knees are
independently programmable. The draintosource voltage drops Vds of transistors ML1, MR1 are kept constant over a wide range of the input voltage Vin, and their magnitudes are fixed by means of the artificially increased offset voltages of the differential amplifiers DAL, DAR. Since these offsets are smaller than the saturation drainsource voltage Vdssat of transistors ML1, MR1, the same are constrained to operate in the triode region. Thus, their transconductance gm, defining the slopes of the trapezoid, is given by:
In figure 2 b), the schematic of differential amplifiers is shown. By sizing
(W/L) M1 > (W/L) M2 a voltage offset between their inputs (V  V+ = Vds) is established and it is linearly controlled by the voltage source Vs as follows:
In this way slopes can be electrically tuned, which is an advantage when analogue storage is available compared to the typical four transistors CFMF operators [2,3]. In the last, input transistors are saturated and tail current Io must be fixed (Io = logical '1'). Even if in both cases slopes are discretely programmed via a set of different sized input transistors, calling N the ratio between the maximum and
minimum desirable slopes, the ratio between the maximum and minimum transistor size needed in our case, from (2), becomes N. For the second case the last ratio is equal to N2, thus, for a given range of slopes, the whole set of saturated input transistors would demand an increased amount of silicon surface. Moreover, in this version of the controller we have performed a combination between a few discrete values of Vs and input transistor sizes in order to optimize the slopes range capability at a low cost in terms of silicon area. Finally, a piecewise expression for the output current Iout of the CFMF is roughly given by (4), where gmL1 and gmR1 result after combining expression (2) and (3) for each
transconductor of the CFMF. In the actual implementation each knee voltage VKR and VKL are obtained by means of a set of binary scaled currents yielding 32 equally spaced voltages drops through a MOSTonly grounded linear resistor. Figure 2 c) shows some measured curves. Note that we could easily get NÃ‹9.5 whereas the input range of Vin reaches to 3V.
2.2. Multipleinput MAX operator
The WinnerTakeAll circuit presented in [5] was adopted for the MAX operator, but some modifications were introduced. The circuit depicted in figure 3 a) is composed by q current controlled voltage sources (M1, M2, M3, M4 and M5) connected to a common node Vc and fighting to impose their own voltage, which is proportional to their controlling current source. Transistors Mc1, Mc2, connected as a cascodediode and common to all cells, convey the highest current at the output. Gate voltages of transistors M1 belonging to the losers fall and those transistors switch off. Diode connected transistors M2, M3 guarantee a voltage level of at least 2VTn at the gate of loser transistors M1. In this way the recovering time delay of these cells (i.e. when any of them pass from loser to winner) is improved. Since transistors M4, M5 are cascoded an accurate replica of the winner current is ensured.
2.3. Consequents and Defuzzifier
Singletons: for the consequent of each rule a discrete singleton ai smaller than 1 is given by: ai = (Cn1) i 21 + (Cn2) i 22 +... + (C0) i 2n, (5) where i ranges from 1 to m and coefficients (Cn1) iÂ¦(C0) i adopt binary values. In figure 1, the outputs of the (n+1) current mirrors of the whole mconsequents set are columnwise summed to give the following (n+1) values:
Except for the above first term, all the others are weighted and summed in the common D/A whose circuit is shown in figure 3 b). Therefore, the output current Iout of the D/A is equal to:
First ideas on common weighting can be found in [11]. Most approaches found in the literature to perform this operation use one individual weighting operator per rule [2] [6]. With the common D/A used here, a big saving of silicon area can be obtained compared to the local D/A approach. On the other hand, also in our case, the input capacitance of each consequent is reduced by a factor (2n/n+1). Additionally, since the layout of the whole defuzzifier becomes smaller routing capacitances are also diminished. As a result, a considerable gain of speed can be achieved. Moreover, in order to improve the matching properties and consequently the accuracy of the converter, the same can be built using nonminimum size transistors without expending too much of silicon area.
Analogue Divider: a novel currentinput voltageoutput divider [4] was specially designed to carry out the division operation in formula (1). The circuit is shown in figure 4a).
With equal sized transistors in each row of the circuit, the division is actually performed by transistors M1, M2, M3 at the bottom layer, all of them being constrained to operate in the triode region. The draintosource voltage drops Vds of those transistors are matched thanks to commongate connected transistors M4, M5, M6 that convey the same current. This is guaranteed by the upper PMOS cascodemirror (M7 to M12). While Vb1 and Vbo are fixed bias voltages, transistor M3 gate voltage Vout is selfadjusted so that the drain current of M6 matches the current imposed by the PMOS cascodedmirror branch M9, M12. In this way, the following relation holds [4]:
Thus, if Vout is referred to Vbo a twoquadrant divider is obtained. Since this divider behaves as a transresistor, there is no need for extra interface converter circuits neither at the inputs [6] nor at the output [7, 8]. Figure 4b) displays some measured characteristics using a HP4145B equipment. Figure 4 c) illustrates the measured relative errors. The output offset (when IN=0) is lower than 1.6mV.
3. Experimental results
In the fabricated twoinputs, oneoutput, ninefixed rules controller, there are three fuzzy labels available per input. Each fourparameters CFMF is 18bits programmable (2x5 bits for knees and 2x4 bits for slopes).
Consequents' singletons are 5bits programmable. Tail current Io was set to 10Ã‚ÂµA. Input voltages range from 1.5V to 4.5V. With Vbo=1.7V and Vb1=2.7V the output voltage ranges between those two values. Figures 5 a) and b) show the simulated and measured output surfaces respectively for a particular setting of the controller. The RMSE between these surfaces remains in 27mV (2.7%). Figures
5 c) and d) illustrate the relative error surface between measured and simulated output values and the distribution of these errors, respectively. Notice from the last figure that most relative errors are concentrated inside a band of Ã‚Â±3%. Also the dispersion between samples due to process fluctuations was characterized and the result from 6 measured prototypes is shown in figure 7 a). Varying from point to point at the output surface, the Standard Deviation features a peak of 62.5mV (6.25%) and a mean value of 35mV (3.5%).
The transient behavior of the controller was typified by measuring the total input/output delay for small and large step signals applied at one of the input while biasing the other with a constant voltage level. In figure 6 a) the amplitude of the input step was set to Vin = 500mVpp whereas the output reacts in 190ns (for the 90% of the steady state value) with a 100mVpp pulse. In figure 6 b) the former experience is repeated but making sweep one input along the whole voltage input range (Vin = 3V). In this case a 500mVpp pulse is settled at the output in 450ns (90%). Therefore, the speed of the controller ranges from
2.22 to 5.26 Mflips for an estimated output load capacitance of CL=13pF. Extrapolating these results for CL=5pF the delay should range from 125ns to 235ns. Consequently up to 8 Mflips would be achieved inside the chip. This last feature must be taken as a proof of the optimal strategies adopted for the design, namely: the avoidance of intermediate voltagetocurrent and/or currenttovoltage converters, the reduced complexity of the defuzzifier and the simplicity of the divider. Figure 7 b) shows the microphotograph of the controller. Its core occupies 3040 x 1500 Ã‚Âµm2 including digital storage circuits that represent almost the 50% of the total area. The measured power consumption rises to 13.4 mW (core: 8.4mW  buffer: 5mW) for Vdd=5V. Table 1 summarizes the attained performances in this prototype.
4. Conclusions
It has been shown that MixedSignal techniques can trade accuracy for a flexibility improvement while holding the advantages of the analogue circuits for massive, parallel and fast computation together with the feasibility of digital circuits for storage. The employment of widespread digital memory circuits to store the digital representation of the controller's parameters simplifies extremely the onchip programming strategy for implementing in a standard CMOS technology. In this way, our prototype behaves as a static RAM for programming purposes whereas signal processing is carried out in the analog domain with a reasonable resolution. Sharing functional operators, particularly fuzzifiers labels and consequents weighting D/A, and performing optimal blocks interfacing by the avoidance of intermediate signal converters (i.e.: currenttovoltage and/or voltagetocurrent converters), have played an important role during the design step. Practiced in depth these general guidelines led to an improved modularity, reflected in smaller silicon area, lower power consumption, reduced storage capacity needed and even shortened internal delays. Experimental results confirm that this controller is suitable for lowpower embedded subsystems for applications with bandwidths below 8Mhz. The use of fast controllers with small number of rules has been reported in several realtime analog applications [1] [9] [10] and their requirements are fairly fulfilled by this prototype.
References:
SBCCI'2001 proceedings  14th Symposium on Integrated Circuits and System Design, Pirenopolis (Brazil), 1015 September 2001, pp. 194200.
Mancuso M., D' Alto V., De Luca R., Poluzzi R. and Rizzotto G., "Fuzzy logic based image processing in IQTV environment", IEEE Trans. on Consumer Electronics, 1995, Vol. 41, N 3, pp. 917923.
RodrÃƒÂguezVÃƒÂ¡zquez A., Vidal F., Linares B. and Delgado M., "Analog CMOS Design of Singleton Fuzzy Controllers", in the 3rd International Conference on Industrial Fuzzy Control Intelligent Systems, (Houston, Texas), December 1993. 
