Design of Hardware IP for 128-Bit Low-Latency Arcsinh and Arccosh Functions

Abstract

With the rapid development of technologies like artificial intelligence, high-performance computing chips are playing an increasingly vital role. The inverse hyperbolic sine and inverse hyperbolic cosine functions are of utmost importance in fields such as image blur and robot joint control. Therefore, there is an urgent need for research into high-precision, high-performance hardware Intellectual Property (IP) for arcsinh and arccosh functions. To address this issue, this paper introduces a novel 128-bit low-latency floating-point hardware IP for arcsinh and arccosh functions, employing an enhanced Coordinate Rotation Digital Computer (CORDIC) algorithm, achieving a computation precision of 113 bits in just 32 computation cycles. This significantly enhances computational efficiency while reducing hardware implementation latency. The results indicate that, when compared to Python standard results, the calculated error of the proposed hardware IP does not exceed 8×10−34. Furthermore, this paper synthesizes the completed IP using the TSMC 65 nm process, with a total IP area of 2.1056 mm2. Operating at a frequency of 300 MHz, its power is 22.4 mW. Finally, hardware implementation and resource analysis are conducted and compared on an Field Programmable Gate Array (FPGA). The results show that the improved algorithm trades a slight area increase for lower latency and higher accuracy. The designed hardware IP is expected to provide a more accurate and efficient computational tool for applications like image processing, thereby advancing technological development.

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

“…Following the execution of perceptrons, the algorithm proceeds to calculate the accuracy of each group, as exemplified by equation ( 1). This accuracy computation serves as a metric to evaluate the effectiveness of the algorithm in identifying significant focal points within each segmented part [54], [55], [56]. It provides a quantitative measure of the algorithm's ability to discern relevant features in relation to the specific Div[i] regions.…”