Deep Learning-Augmented Design of Floating-Point Arithmetic Units for High-Performance Nonlinear Computations

Abstract

Nonlinear computations plays a critical role in modern applications such as deep learning, digital signal processing, cryptography, and scientific modelling, yet conventional IEEE-754 floating-point units struggle to meet the conflicting demands of high precision, low latency, and energy efficiency. Static data paths, fixed-precision formats, and polynomial-based approximations limit their adaptability and numerical robustness, particularly for transcendental and nonlinear operations. In this work, a deep learning–augmented floating-point arithmetic architecture is proposed to address these limitations. The architecture integrates five complementary learning-enabled modules: adaptive latency optimization using a reinforcement learning–based micro-scheduler, context-aware runtime error estimation and compensation, dynamic bit-width control through precision-aware deep bandwidth scaling, gradient-informed neural approximation of nonlinear functions, and domain-aware transfer learning for rapid arithmetic unit adaptation across applications. The proposed design is evaluated using FPGA-based hardware–software co-simulation on representative workloads from neural networks, signal processing, and cryptographic domains. Experimental results show substantial improvements, including latency reductions of up to 63%, numerical error reduction exceeding 70% with accuracy within ±0.5 ULP, power savings of up to 59%, and area reductions beyond 40%. Furthermore, application-level validation demonstrates negligible degradation in inference accuracy and significant reductions in retraining overhead across domains. These results confirm that learning-augmented arithmetic units provide an effective and scalable solution for next-generation nonlinear floating-point computation.