## 1 Introduction  
Progress in nuclear power technology heavily depends on the expansion of computing performance. The cutting-edge multiphysics [1; 2] and multiscale frameworks [3; 4; 5], designed to provide more realistic predictions of a reactor core's safety parameters and to unveil the fundamental mechanisms of nuclear fuel behaviour beyond heuristic correlations, require higher-than-ever computing resources. Over decades, the advance in computing performance has been achieved through transistor miniaturization, which translates into the improvement of the processor design, clock rate speed-up and the high-density multicore architecture. However, nowadays the processor architecture scale is close to its physical limit and it becomes more difficult to shrink [6]. New computing architectures and models will thus be required to continue gaining improvements in chip performance. Optical computing [7], DNA computing [8], coupled oscillating networks [9], approximate computing [10], quantum computing [11] and analogue-digital in-memory computing [12; 13] have become important areas of exploration. Another big advance in computing performance is envisioned with the use of traditional computing hardware accelerated for specific engineering tasks. The hardware acceleration of nuclear engineering applications can rely on machine learning methods and approximate computing techniques aimed to trade off the computational quality with speed/energy efficiency. One of the most promising strategies of approximate computing is the use of a smaller numerical precision in floating-point operations. This idea is motivated by the fact that a high precision of computing hardware is often excessive considering a much lower accuracy is required in practice for engineering applications. Moreover, the solution accuracy is usually limited by physical approximations, numerical errors and the uncertainty of the input data. Therefore, the computing requirement can be decreased in order to redirect the resources into higher spatial, energy or angular resolution or simply to improve the statistics in simulations. This idea has proven to be beneficial for several applications of ocean, weather and climate modelling [14; 15] as well as for neural networks training [16]. For a full survey of approximate computing techniques, including their challenges and obstacles, one can read [10]. The usage of a lower precision provides three advantages that can make computation faster. Firstly, a floating-point unit processor requires less work per operation. As an example, switching from double to half precision reduces complexity almost 9 times assuming the best-known Harvey-Hoeven binary multiplication algorithm [17] of complexity \(O(n\log n)\) and 14 times for the Karatsuba algorithm [18] of complexity \(O(n^{1.885})\). Secondly, more data can fit in a chip's cache allowing to speed up memory-bound computations, whose performance depends primarily on the time cost to access memory storing working data. The larger the cache size, the greater number of accesses to the cache that actually find data, hence, the average memory access time becomes less. Empirical studies [19] have shown that the cache miss rate decreases as a power law of the cache size, where the power lies between \(-0.3\) and \(-0.7\), which is equivalent to decreasing by a factor of two if the precision switches from double to half. Thirdly, the lower precision facilitates the communication between parallel processors, whose impairment can seriously slow down computations [20]. Anticipating all these advantages, the low- and multiprecision architectures are now considered as prospective techniques for the acceleration of large-scale applications on the forthcoming high-performance computer systems. In this paper, we investigated the advantage of low-precision arithmetic for nuclear engineering applications.