Efficient GF(2 m ) arithmetic clearly affects the performance of compute-intensive applications. A new low-complexity parallel-in/ out systolic AB 2 multiplier based on the least significant bit-first scheme is presented. Compared with related works, the scheme yields significantly lower area-time complexity.Introduction: Finite fields GF(2 m ) have taken a keen interest owing to their useful applications in coding theory, cryptography, and signal processing [1, 2]. Among GF(2 m ) operations, time-consuming inversion and exponentiation can be computed efficiently by a sequence ofwhere only m − 1 AB 2 -type multiplications are performed recursively. However, if general multiplication is used instead, m − 1 multiplications and m − 1 squarings are needed in the worst case. Some effort have been made on efficient realisation of AB 2 [3-5]. Systolic designs provide area-time (AT) efficient implementation due to modularity and regularity of their structures. Wei [3] proposed a systolic AB 2 with two-way data flow, whereas Wang-Guo [4] and Kim-Lee [5] proposed a systolic AB 2 with one-way data flow. Although several systolic AB 2 have been proposed, their high area and time complexities are crucial constraints. Thus, further study for efficient AB 2 architecture with low complexity is required. In this Letter, a new low-complexity parallel in/out systolic AB 2 architecture in GF(2 m ) is presented, which is on the basis of least significant bit (LSB)-first method. It can be utilised as a major component for both inversion and exponentiation.

As energy consumption in high-performance systems has increased, thermal management has become a big challenge. Providing a cost-effective and detailed temperature sensing mechanism is crucial to effectively employ a thermal management technique. Existing hardware sensors are too costly to implement and add additional heat while software simulations fail to account for all possible hardware effects. In this paper, we describe a software solution for temperature sensing that uses real hardware resources such as performance counters. The resulting temperature model provides a detailed spatial gradient of the processor and executes at runtime. In particular, the model is configured for the Pentium 4 processor. We run SPEC2000 benchmarks to analyze the thermal behavior of applications and explain the potential benefits of using our model for temperatureaware research.