Using standard Floating-Point (FP) formats for computation leads to significant hardware overhead since these formats are over-designed for error-resilient workloads such as iterative algorithms. Hence, hardware FP Unit (FPU) architectures need run-time variable precision capabilities. In this work, we propose a new method and an FPU architecture that enable designers to dynamically tune FP computations’ precision automatically at run-time called Variable Precision in Time (VPT), leading to significant power consumption, execution time, and energy savings. In spite of its circuit area overhead, the proposed approach simplifies the integration of variable precision in existing software workloads at any level of the software stack (OS, RTOS, or application-level): it only requires lightweight software support and solely relies on traditional assembly instructions, without the need for a specialized compiler or custom instructions. We apply the technique on the Jacobi and the Gauss–Seidel iterative methods taking full advantage of the suggested FPU. For each algorithm, two modified versions are proposed: a conservative version and a relaxed one. Both algorithms are analyzed and compared statistically to understand the effects of VPT on iterative applications. The implementations demonstrate up to 70.67% power consumption saving, up to 59.80% execution time saving, and up to 88.20% total energy saving w.r.t the reference double precision implementation, and with no accuracy loss.

Functional verification increasingly relies on Assertion-Based Verification (ABV), which has become a key approach for verifying hardware designs due to its efficiency and effectiveness. Central to ABV are automatic assertion miners, which apply different techniques to generate assertions automatically. This paper reviews the most recent, advanced, and widely adopted assertion miners, offering a comparative analysis of their methodologies. The goal is to provide researchers and verification practitioners with insights into the capabilities and limitations of existing miners. By identifying their shortcomings, this work also points toward directions for developing more powerful and advanced assertion miners in the future.