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An applications of signal processing are frequently used everywhere in daytoday life. “Digital Signal Processing (DSP)” has been basic requirement of efficient and accurate arithmetic operations for performing fast and accurate signal processing. Logarithm arithmetic provides an option of that desire. In this work, it is presented an efficient VLSI implementation of an antilogarithm converter by using 10region error correction. It provides error efficient implementation with significant hardware gain. VLSI implementation of reported and proposed antilogarithmic converters design created in Xilinx ISE 12.1. Antilogarithmic converters (reported and proposed design) are synthesized by using Synopsys design software perform the RTL Synthesis analysis by using package design compiler. This paper presents 10 region converter which considers design tradeoff where proposed demonstrates 19.75%, 31.02%, 12.65%, 44.65% and 29.91% respective reduction in comparison of previous design. Error analysis was done using MATLAB for proposed conversion method and reported methods. Suggested antilogarithmic converters have 1.559% error only in comparison of 1.7327% error reported by Kuo et al. On behalf of hardware complexity and error analysis results, it can say that the proposed converters could perform better in comparisons of all aspects of reported design.
antilogarithmic converter, digital signal processing, error analysis, logarithmic converter, logarithmic multiplication, Mitchell method
A DSP application in a real time domain is a superset of an arithmetic operation. From literature, it is well known fact that binary arithmetic is accurate and hardware efficient [1, 2]. Logarithm offers an option to digital designers that they can substitute of binary operations over arithmetic. It performs multiplications operation via additions, due to this process lot of computation efforts save in comparison of reported arithmetic [35]. Logarithmic operations have nowadays become a good choice because it has an efficient hardware architecture in comparisons of binary arithmetic operations but, it has less accuracy in comparison with binary arithmetic [6].
Logarithmic number system (LNS) arithmetic operations are consisting of following essential stages: Logarithm conversion; arithmetic operation stages; and vice versa [7, 8]. Method of converting logarithmic/antilogarithmic is dominating factor of performance in terms of hardware and accuracy efficiency of LNS based arithmetic circuits [9, 10]. Antilogarithmic conversion is an accuracy deciding factor, so it created necessity of accurate converter to speed up arithmetic operation performance [11, 12].
In 1962, for the first time, Mitchell proposed add and shifted operationbased algorithm for multiplication by using the single region piecewise linear approximation. It presented first time a logarithmic and antilogarithmic converter. The “Average Error Percentage (AEP)” was 3.85% whereas the error range was varying up to 11.11%. It yielded maximum decrement in percentage error from 11.1 to 1.3 with high accuracy considering identical time penalty of speed with high level power requirement, and “hardware complexity” [6]. Error percent of Mitchell’s method 1region antilogarithm converter are displayed in Figure 1.
Figure 1. Mitchell’s method by using 1region antilogarithm converter with error percent
In 1970, multiple regions linear approximation was proposed by Hall et al. [13], but main drawback of this design is suffered from hardware inefficiency. “Hall’s algorithm” used all bits in the mantissa for error adjustment and for achieving this applying linear piecewise approximation. In 1999, SanGregory et al. [14] presented small and fast algorithm using four “Most Significant Bits (MSB)s” of mantissa for bits adjustment. The “antilogarithm” equation by using “2region correction” proposed by SanGregory’s was the conversion methods which improved accuracy of previous algorithms using “Read Only Memory (ROM)”. Multiple region logarithmic / antilogarithmic converter proposed by Abed et al. [11, 12] with low errors, at the penalty of higher area costs. It offered on the utilization of ROM circuits for hardware minimization. The highest obtained error during of this conversion process was up to 1.5%. Abed and Siferd suggested correction that produced better results in term of accuracy, speed, and complexity. Correction strategies used for 2, 6, and 7 regions with changing hardware complexity and accuracy [12]. In 2004, a 2region error manipulation schemes were proposed by Juang et al. [15, 16] and advancement of this work is introduced for logarithmic and antilogarithmic converter. Juang et al. [16] have proposed 2region correction by using bit level manipulation schemes to achieve tradeoff. Same concept is used for an “antilogarithm conversion”. 2 region logarithm approximations maximum error ranges from 0 to 0.0319 and 0.60 to 1.72 for “antilogarithm converter”, respectively. In 2012, Kuo and Juang [17] proposed fourregion “antilogarithmic converter” using “piecewiselinear approximation”. In 2015, Juang et al. [18, 19] and in 2016, Durgesh et al. presented 2region antilogarithm approximation in antilogarithm converter for the range of 0.60 to 1.72 where recorded error range was 2.32%. Furthermore, in 2016, Kuo et al. [20] presented for multiple regions by using constant compensation scheme for error minimization which reported 1.83%, 1.34% and 0.61% error, respectively with multiple regions at the cost of hardware penalty. In 2017, Durgesh et al. [21] suggested efficient architecture of antilogarithm converter for 11region at same error cost . In 2019, Durgesh and his research team suggested 16 regions based “error correction scheme” for “antilogarithm converter” [22]. In 2020, Durgesh and his research team suggested multi regions “error correction scheme” “antilogarithm converter” for DSP processor [23]. For error minimization circuit in antilogarithm converter when it is increases the number of regions, hardware cost increasing and errors are decreasing. It motivates the researchers to work for best alternatives with fulfill the tradeoff between accuracy and hardware cost. In this paper, it is proposed a 10region antilogarithmic converter with error correction scheme where the balance between exactness and hardware costs has been met.
The arrangement of an article is like that: Section 3 presents reported antilogarithmic mathematical concepts. Section 4 describes proposed methodology and detailed hardware architecture. Section 5 explains error and hardware efficiency outcomes and comparative review. The paper is concluded in section 6 where outcomes and limitations of proposed antilogarithmic converter are elaborated.
Let $Z$ be a binary number for fixed interval range $2^{a+1} \leq$ $Z \geq 2^{b}$ and $a \geq b$. Now, the $Z$ will be represented as Eq. (2):
$Z={{2}^{a}}(1+\sum\limits_{i=b}^{a1}{^{\mathop{2}^{ia}}}{{z}_{i}})$ (1)
where, $a$ is considered as fraction part and its lies between 0 to 1. The logarithm called true logarithm of Z is represented by:
$\lg (Z)true=a+\lg (1+x)$ (2)
The $\lg (1+x)$ value was approximated by Mitchell's method. $x$ is the fraction value called as approx logarithm and it's represented in Eq. (3):
$\lg (Z)approx=a+x$ (3)
The antilogarithm results the final product, $P$ logarithm which is represented as follows:
$P{{=}_{2}}a\text{ }(1+x)$ (4)
Kuo et al. proposed constant compensation scheme for error minimization. It achieved error was 1.83%, 1.34% and 0.61% with “11region”, “14region” and “28region”, respectively [20]. Eq. (5) shows the constant compensation scheme and Eq. (6) shows the compensation value (c).
$\begin{align} & \text{Antilog(A)=}{{\text{2}}^{\text{A}}}\text{=}{{\text{2}}^{\text{p}}}\times {{2}^{m}}={{2}^{m}}(1+m+c\text{), } \\ & \text{Where 0}\le m\text{1} \\ \end{align}$ (5)
$\text{c = }\pm \sum\limits_{j}{{{2}^{j}}}\text{,}$ (6)
where, j denotes positive numbers.
In order to improve the error in any straight line, an approach has been employed in which addition of difference between “approximated value” and “actual value”. But, the simple process of adding the difference of “approximated value” minus “actual value” is not able to much exact result. Also, in many cases it is observed that the variation of error in different segment results different percentage of error, i.e. error is not uniform in all condition [18]. For substantial error minimization, split straight lines into several regions and add the average correction difference in a given range. The key concern, however, is that increased hardware penalty is increased in subdivisions. Our main objective of this paper is error minimization with significant hardware gain.
4.1 Proposed method
The proposed method is approximated by the following Eq. (7), and “error percent” is representing in Eq. (8). It is based on the piecewiselinear approximation concepts.
$\text{Y}={{2}^{k}}{{2}^{m'}}={{2}^{k}}(ax+b)$ (7)
$\text{Percent Error}=(\frac{(ax+b){{2}^{x}}}{{{2}^{x}}})\times 100,\text{ 0}\le \text{x1}$ (8)
Here, Percentage error is explained as the approximated value percentage minus actual value divided by actual value. The approximation errors for each sub regions can be obtained using Mitchell’s method [5]. The proposed design of antilogarithmic converter may be formulated as Eq. (9) and error correction value (ECV) can be like as Eq. (10).
$Anti\log (A)={{2}^{A}}={{2}^{p}}\times {{2}^{x}}={{2}^{x}}(1+x+c),\text{ }0\le x<1$ (9)
$\text{ECV = c = }\pm \sum\limits_{i}{{{2}^{i}}},$ (10)
where, i is a positive integer. The Figure 2 shows the algorithm for the obtainment of optimum error coefficients in the proposed Antilogarithmic converter.
Figure 2. The optimal error correction coefficient algorithm
We can use the proposed algorithm for finding “error correction coefficient” for the “10regions” “antilogarithmic converter”. By using a given algorithm in Figure 2, 10regions of “approximate schemes” can be represented as Eq. (11), and the “compensation values” as shown below in Table 1.
For easy understanding, if N=10, then ‘K’ can be partitioned into [0, 0.055), [0.055, 0.115), [0.115, 0.187), [0.187, 0.260), [0.260, 0.374), [0.374, 0.670), [0.670, 0.78), [0.78, 0.875), [0.875, 0.955) and [0.955, 1.00), respectively. Here, ‘i’ partitioned in that way which can create a minimum error as well as fewer hardware requirements also. After extracting the “optimal error correction” coefficient values for every region, the manual adjustment process is adopted to fine tune the process in order to get the minimum “percent errors” for entire range.
${{\text{A}}_{\text{proposed}}}\text{=}{{\text{2}}^{\text{p}}}\times {{2}^{x}}\approx {{2}^{\text{p}}}(1+x+\text{c), 0}\le \text{x1}$ (11)
By the help of Table 2, for the corrected values of 2^{4}, 2^{5}, 2^{6}, and 2^{7 } Kmap can be drawn.
For 10 regions corrected values of 2^{4}, 2^{5}, 2^{6} and 2^{7}.
s1= m_{1} and m_{2};
s2= s1 and (not m_{3}) and m_{4};
s3= m_{1} and (not m_{2});
s4= (not m_{1}) and m_{2};
n_{4}= s3 or (s4 and m_{4}) or (m_{3} and s4);
n_{5 }= (s4 and m_{3} and m_{4}) or s2 or ((not m_{1}) and (not m_{2}) and m_{3});
n_{6 }= s1 or (s4 and (not m_{3}) and (not m_{4}) or ((not m_{2}) and (not _{m3}) and m_{4}) or (s3 and (not m_{3})); n_{7} = s2;
On the basis of simplified equation found by kmap we can able to decide the hardware architecture of circuit.
4.2 Hardware implementation
The proposed “antilogarithm converter” architecture by using “10region” “error correction scheme” is shown in Figure 3. Part (a) of Figure 3 shows the architecture of subcomponent which has 4 inputs and 4 outputs based on Kmap of corrected values and (b) provides the principal block of 10region error correction circuit. It is combination of “Full subtraction (FS)”, “Half subtraction (HS)” and “Modified subtraction (MS)”.
Table 1. Subtraction terms of various regions for proposed “antilogarithm converters” using “10region error correction schemes
Items 
“Fractional region” 
Subtraction terms 
Items 
“Fractional region” 
Subtraction terms 
1 
[0, 0.055) 
0 
6 
[0.374, 0.670) 
(1/16+1/64) 
2 
[0.055, 0.115) 
(1/64) 
7 
[0.670, 0.780) 
(1/16) 
3 
[0.115, 0.187) 
(1/32) 
8 
[0.780, 0.875) 
(1/32+1/128) 
4 
[0.187, 0.260) 
(1/32+ 1/64) 
9 
[0.875, 0.955) 
(1/64) 
5 
[0.260, 0.374) 
(1/16) 
10 
[0.955, 1) 
0 
Table 2. 10 region corrected values
m_{1}(=0) m_{2} m_{3}m_{4} 
“Corrected value” 
m_{1}(=1) m_{2} m_{3}m_{4} 
“Corrected value” 

2^{4} 
2^{5} 
2^{6} 
2^{7} 
2^{4} 
2^{5} 
2^{6} 
2^{7} 

000 
0 
0 
0 
0 
001 
1 
0 
1 
0 
001 
0 
0 
1 
0 
010 
1 
0 
1 
0 
010 
0 
1 
0 
0 
011 
1 
0 
1 
0 
011 
0 
1 
0 
0 
100 
1 
0 
0 
0 
100 
0 
1 
1 
0 
101 
0 
0 
0 
0 
101 
1 
0 
0 
0 
110 
0 
1 
0 
1 
110 
1 
0 
1 
0 
111 
0 
0 
1 
0 
111 
1 
0 
1 
0 
000 
0 
0 
1 
0 
(a)
(b)
Figure 3. (a) “Combined circuit” of corrected value and (b) Principal block of 10region error correction circuit
4.3 Error analysis
As we realize that Error investigation is the primary worry in logarithm arithmetic on the grounds that it has effectively limited design in correlations of binary arithmetic. Here, based on available equation, simulation is done by MATLAB software. The MATLAB graphs for the proposed method with 10regions error corrector scheme and antilogarithm converter using 11 regions converter with constant compensation scheme is shown in Figure 4.
Figure 4. Analysis of percent error by using proposed 10region error correction circuit with reported 11region
By using proposed method every “subregion” has less “error percentage” in comparison to past presented method. Error will be calculated based on three parameters, the “Maximum Positive Percent Error (MPPE)”, “Maximum Negative Percent Error (MPPE)” and the “Percentage Error Range (PER)”. Table 3 has been shown findings. The proposed method by using 10regions “error correction scheme” gives 1.554% in comparisons of “Mitchell’s” 6.1476%, “Juang” et al. “2region” 1.72% and “Kuo” et al. “11region” 1.7327% MPPE and gives 0.00509% in comparisons of “Mitchell’s” 0%, Juang et al. “2region” 0.6% and Kuo et al. “10region” 0.0992% MNPE. The proposed method by using “10regions” “error correction scheme” gives 1.559% in comparisons of “Mitchell’s” 6.1476%, “Juang” et al. “2region” 2.3232% and “Kuo” et al. “11region” 1.8319%. After careful observation of Table 3, it found that proposed method has 24.89% less “error percentage” in comparisons of existing designs [16].
Table 3. Error comparisons table of various reported and proposed architecture

Regions 
MPPE 
MNPE 
PER 
Mitchell’s straightline ^{5} 
1 
6.1476 
0 
6.1476 
Juang ^{14} 
2 
1.72 
0.6 
2.3232 
Kuo ^{16} 
11 
1.7327 
0.0992 
1.8319 
Proposed (10Regions) 
10 
1.554 
0.00509 
1.559 
Tabularly 4 presents theoretical and hardwarecomplexity analyses for simple gates and reported structures for 10regions antilogarithm error correction scheme. It is revealed from the Table 4 that the employed set of gates i.e., AND, OR, NOT and XOR in the proposed antilogarithm converter structure by utilizing 10regions error correction scheme are 25,11, 15 and 10, respectively whereas in 11 region constant compensation scheme the employed gates were 35, 15, 26 and 13, respectively. It means that the proposed structure employs 10 less “AND” gates, 4 less “OR” gate, 11 less “NOT” gates and 3 less “XOR” gates. Consequently, through study of “theoretical hardware complexities”, we can say that the “10region” “error correction scheme” proposed is more hardware efficient compared to the “11region” permanent compensation scheme that has been published.
5.1 Synthesis results
The “Synopsys design compiler” with “TSMC 65 nm CMOS library” is employed in order to design and evaluate the proposed antilogarithm converter by using 10regions structure and 11region “constant compensation scheme”, was employed. The parameters, “Data arrival time (DAT)”, area, power, “Area delay product (ADP)”, and Energy, are compared for the proposed and the reported structures [14] and [16] are as listed in Table 5. The reported results for proposed “antilogarithm converter” by using “10regions error corrector scheme” shows 19.75%, 31.02%, 12.65%, 44.65% and 29.91% improvement in DAT, area, Power, ADP and engery respectively when compared to the existing antilogarithmic converter scheme in [16]. On the basis of error analysis, theoretical hardware complexity analysis and simulation results, it can well understand that proposed “10 regions” “antilogarithm converter” is better than reported antilogarithm converter in every aspect.
Table 4. General comparisons of “hardware complexities” of reported 11regions and proposed “10regions error correction circuit”
Structure 
Antilogarithm Existing^{16} 
Antilogarithm Proposed 
AND 
35 
25 
OR 
15 
11 
NOT 
26 
15 
XOR 
13 
10 
Table 5. Synopsys DC Compiler synthesis results of proposed 10region error correction circuit and reported structures
Structure 
DAT (ns) 
Area (µm^{2}) 
Power (µW) 
ADP (µm^{2}*n) 
% gain in ADP 
Energy (n J) 
% gain in Energy 
Reported (11region) ^{16} 
0.81 
203.04 
4.068 
164.46 
 
3295.08 
 
Reported (2region) ^{14} 
0.67 
109.80 
3.393 
73.56 
 
2273.31 
 
Proposed 
0.65 
140.04 
3.553 
91.02 
44.65 
2309.45 
29.91 
In this paper, “antilogarithmic converter” by using 10region “error correction schemes” had suggested with best possible tradeoff in comparison of reported designs. It demonstrates 24.898% percentage error reduction when compared to the proposed design of Juang et al. [16]. In terms of ADP and low energy correlations, the proposed converter with 10regions shows 44.65% and 29.91%, respectively from existing 11regions antilogarithm converter. It has 32.89% less error at the cost of 23.73% ADP penalty in comparison to Juang et al. 2region “antilogarithm converter”. The proposed 10region “error correction schemes” for “antilogarithmic converter” can be used for realtime DSP based applications.
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