A Survey on True Random Number Generators Based on Chaos

Table 1

Summary of TRNGs methods based on chaos.

Classification	Author and reference	Area (mm^2)	Power (mW)	Out bit rate (speed) (Mbit/s)	Energy (PJ/bit)	Post processing	Test suite	Technology
Chua’s system	Moqadasi [44]	N/A	N/A	2.02	N/A	6 bit LFSR	FIPS 140-1	0.18 µm CMOS
Jerk system	Wannaboon [46]	0.037689	1.32	50	26.4	Von Neumann	NIST SP800-22 and TestU01	0.18 µm CMOS
Boolean chaotic oscillator	Park [48]	0.057	26.1	300	87	XOR	NIST	0.35 µm CMOS
Coupled chaotic oscillator	Ozoguz [51]	N/A	N/A	2	N/A	Von Neumann	FIPS 140-1 and NIST	0.35 µm CMOS
FPGA-based	Akgul [54]	N/A	N/A	4.59	N/A	XOR	FIPS 140-1 and NIST SP800-22	FPGA
Logistic mapping	Avaroglu [56]	N/A	N/A	20	N/A	RO and inverter number	NIST SP800-22 and TestU01	FPGA
Logistic mapping	Teh [57]	N/A	N/A	447.83	N/A	XOR and32-bit addition	NIST SP 800-22	CPU
Tent mapping	Angulo [62]	0.07	0.15	0.25	800	8 bit LFSR	NIST	0.35 µm CMOS
Bernoulli mapping	Cicek [65]	N/A	125	1.5	83300	N/A	NIST SP800-22	FPGA
PWAM mapping	Pareschi [68]	0.752	29	40	0.725	N/A	NIST SP800-22	0.35 µm CMOS
Discrete-timechaotic oscillator	Dhanuskodi [69]	93.1	1.0967	127	8	XOR	NIST	0.45 µm CMOS
Current-modechaos	Katz [80]	0.02	0.8	25	32	N/A	FIPS 140-2	0.09 µm CMOS