The Floating Point Trinity: A Multimodal Approach to Extreme EnergyEfficiency and Performance The demand for floatingpoint compute power is ever growing. The domains of bigdata, machine learning, and scientific computing require a wide precision range and high operational intensity. The sheer number of operations paired with increased power density implied by technology scaling makes it more important than ever to achieve maximum energyefficiency for floating point operations. In this work, we present Kosmodrom, our novel silicon solution in Globalfoundries 22 nm FullyDepleted Silicon on Insulator (FDSOI) which offers a multidimensional approach to tradeoff performance, energyefficiency and power consumption. A variableprecision, dualcore RISCV system together with a specialized floating point accelerator form the architectural basis. Different implementation strategies and standard cell flavors provide optimal solutions for different operating conditions while supply voltage and forward body bias (FBB) enable for a dynamic tradeoff during operation. In this work, we provide a unique insight into the impact of a multitude of tuning parameters to achieve the optimal operating point on the powerperformance surface. Kosmodrom achieves a peak energyefficiency of 260GflopsW and up to 28Gflops peak performance within a 6.2400mW power envelope.

Performance of Floatingpoint Intensive Kernels on Lowpower Processor xe2x80x93 A Case Study with Geodesic Distance Kernel A processor, with a GPU and a CPU integrated on the same chip, is a promising lowpower system for floating point intensive applications. While an integrated GPU is not designed to outperform a discrete GPU due to its power, area, and thermal constraints, there is a need to better understand the performance of a floatingpoint intensive kernel using an integrated GPU. Toward this end, we choose a representative floatingpoint intensive kernel as a case study. We port the kernel with a vendorneutral framework, analyze the compiler optimizations of the kernel at the assembly code, evaluate the relationship between floatingpoint operations per second and arithmetic intensity, and compare the performance and power of the kernel implementations on the CPU and GPU. Our key findings are: 1) Compared to an unoptimized kernel, the floatingpoint optimizations improve the performance of the single and doubleprecision floatingpoint kernels executing on an Intelxc2xae GEN8 Iris Pro GPU by 15.4X and 5.4X, respectively; the optimizations also improve the performance of the two kernels by 5.6X and 3.4X on an Intelxc2xae Xeonxc2xae E3 CPU, respectively. 2) Achieving peak floatingpoint operations per second on the GPU requires much higher arithmetic intensity than that on the CPU. 3) Running the floatingpoint intensive kernel on the processor consumes 48 Watts, which is very close to the thermal power draw of the processor. The floatingpoint optimization can reduce the average GPU power from 35.7 W to 22.7 W for the doubleprecision kernel, and from 33.1 W to 8.8 W for the singleprecision kernel.