Intel(US:INTC) Xin Lang Cai Jing·2026-01-18 06:06Core Viewpoint - The article discusses the importance of random number generators (RNG) in computing, detailing the types of RNGs, their mechanisms, and applications in various fields [1][2][5]. Group 1: Types of Random Number Generators - True Random Number Generators (TRNG) generate unpredictable and non-repetitive random numbers based on physical processes, but they have limitations in speed and efficiency [2][5]. - Pseudo Random Number Generators (PRNG) use mathematical algorithms to produce sequences that mimic the statistical properties of true random numbers, often seeded by TRNG outputs to enhance quality [3][4]. Group 2: Detection Methods - Various methods exist to validate the randomness of TRNG and PRNG outputs, including statistical tests such as bit distribution, run tests, and collision detection [5][6]. - Standards for randomness testing include GB/T 32915-2016 in China, NIST SP800-22r1a in the USA, and A1S31 in Germany, which provide frameworks for assessing the quality of random number sequences [5][6]. Group 3: Applications - Random numbers are crucial in cryptography for generating secure encryption keys, ensuring data protection [9]. - They are also used in mathematical simulations to model uncertainty, in game development for creating random events, in computer graphics for generating textures, and in statistics for conducting randomized trials and sampling [9].