# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 5 Experimental Results
### Chip Performance Comparison  
This paper compares the performance of various CORDIC hardware architectures (Table 9). The first comparison is a look-up architecture circuit. [PERSON] [30] mainly uses the combined multi-constant multiplier (MMCM) and Wallace multiplier to perform the architecture. Both MMCM and fused butterfly with Wallace multiplier (FBW) adopt 45 nm VLSI processes. The operating frequency is 200 MHz, the areas are 101,312 \(\upmu\)m\({}^{2}\) and 116,886 \(\upmu\)m\({}^{2}\), and the power consumption of the chip is 127.13 mW and 154.78 mW. Our experiment uses an 0.18 \(\upmu\)m VLSI process. According to the scaling factor rule of the VLSI process, if the chip is fabricated using a 45 nm process (Table 10), the operating frequency can be 400 MHz and the area is 54,997.6 \(\upmu\)m\({}^{2}\), and the power consumption is 2.656 mW. The second comparison is with the standard CORDIC algorithm. [PERSON] [25] uses the conversion between polar coordinates and rectangular coordinates to calculate the sine and cosine, which are rotated according to the standard method. Therefore, the maximum selection angle is 16 times, the process is 65 nm, the operating frequency is 35 MHz, the area is 863,300 \(\upmu\)m\({}^{2}\), and the power consumption is 145.33 mW. According to the scaling factor rule of the VLSI process, if the chip is fabricated using a 65 nm process, the operating frequency can be 276 MHz, the area is 109,995.25 \(\upmu\)m\({}^{2}\), and the power consumption is 5.312 mW. [PERSON] [26] proposed a CORDIC-based architecture that uses coordinate computer algorithms to achieve various operations. The process is 0.18 \(\upmu\)m, the maximum rotation is 16 times, and the operating frequency is 40 MHz. The area is 1,194,648 \(\upmu\)m\({}^{2}\) and the power consumption is 51 mW. [PERSON] [27] mainly uses discrete Fourier transform to improve the calculation speed of CORDIC. The maximum operating frequency is 125 MHz, the total area is 11,000,000 \(\upmu\)m\({}^{2}\), the power consumption is 350 mW, and the number of rotations is 16. The proposed chip is used to reduce the number of rotation iterations of the CORDIC calculation. After the angle input, the number of iterations can be reduced by more than half, and the area is also  
Figure 28: Microphotograph of the chip. \begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Technology & TSMC 0.18 \(\upmu\)m 1P6M \\ Chip Size & 1.93 mm \(\times\) 1.93 mm \\ Core Size & 1.32 mm \(\times\) 1.32 mm \\ Package & CQFP 100 \\ Gate Counts & 129,688 \\ Frequency & 100 MHz \\ Scan Chain & 1 \\ Fault Coverage & 99.44\% \\ Power Supply & 1.8V \\ Power Consumption & 237.34 mW@100 MHz \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Chip specification. the smallest under the same process. Table 10 illustrates the process conversion table according to the VLSI scaling factor rule. In this section, the proposed chip improves the traditional CORDIC calculator. Originally, it takes 0.094 s to calculate a packet and 7.05 s to decode a whole picture that has a total of 75 packets. The proposed chip takes only 0.012 ms to solve a packet and 0.912 ms to decode a frame. The throughput of the chip is \(8.2105\times 10^{8}\) bits/sec. The throughputs of the literature [PERSON] [25], [PERSON] [26] and [PERSON] [27] are \(1.2572\times 10^{8}\) bits/sec, \(1.4368\times 10^{8}\) bits/sec and \(4.4901\times 10^{8}\) bits/sec respectively. The method we have proposed to reduce the number of iterations, the performance of throughput is much higher than other methods in the literature [25; 26; 27]. To verify that the reconstructed point clouds are correct, we compare with Velodyne LiDAR VLP-16 ground truth data that includes County Fair, Hecker Pass, and Monterey Highway. Moreover, in order to compare the correctness of the decoding of LiDAR packet data, we compare the decoding results of the standard test environment of VeloView 2.0.