A Forward-Backward Iterative Procedure for Improving the Resolution of Resonant Microwave Sensors

This paper sets out a method for improving the resolution of resonant microwave sensors. Usually, the frequency response of these devices is associated with a low quality factor, and consequently with a low resolution in terms of tracking capacity of the resonance frequency shift. Furthermore, since only a finite number of samples can be acquired during the measurement process, the “true” resonance frequency may not be included in the set of acquired data. In order to have an accurate estimate of the resonance frequency, high performance systems with very fine frequency sampling are thus required. To limit these drawbacks, an iterative algorithm is presented which aims to refine the response of resonant microwave sensors by means of a suitable post-processing. The algorithm evaluation is first carried out on synthetic data, and then applied on experimental data referring to a practical scenario, which is inherent to return loss measurements performed by a microwave patch antenna immersed in a water-glucose solution with different concentrations.

Enhancing the Scalability of Multi-FPGA Stencil Computations via Highly Optimized HDL Components

Stencil-based algorithms are a relevant class of computational kernels in high-performance systems, as they appear in a plethora of fields, from image processing to seismic simulations, from numerical methods to physical modeling. Among the various incarnations of stencil-based computations, Iterative Stencil Loops (ISLs) and Convolutional Neural Networks (CNNs) represent two well-known examples of kernels belonging to the stencil class. Indeed, ISLs apply the same stencil several times until convergence, while CNN layers leverage stencils to extract features from an image. The computationally intensive essence of ISLs, CNNs, and in general stencil-based workloads, requires solutions able to produce efficient implementations in terms of throughput and power efficiency. In this context, FPGAs are ideal candidates for such workloads, as they allow design architectures tailored to the stencil regular computational pattern. Moreover, the ever-growing need for performance enhancement leads FPGA-based architectures to scale to multiple devices to benefit from a distributed acceleration. For this reason, we propose a library of HDL components to effectively compute ISLs and CNNs inference on FPGA, along with a scalable multi-FPGA architecture, based on custom PCB interconnects. Our solution eases the design flow and guarantees both scalability and performance competitive with state-of-the-art works.

On Solving the Decycling Problem in a Torus Network

Modern supercomputers are massively parallel systems: they embody thousands of computing nodes and sometimes several millions. The torus topology has proven very popular for the interconnect of these high-performance systems. Notably, this network topology is employed by the supercomputer ranked number one in the world as of November 2020, the supercomputer Fugaku. Given the high number of compute nodes in such systems, efficient parallel processing is critical to maximise the computing performance. It is well known that cycles harm the parallel processing capacity of systems: for instance, deadlocks and starvations are two notorious issues of parallel computing that are directly linked to the presence of cycles. Hence, network decycling is an important issue, and it has been extensively discussed in the literature. We describe in this paper a decycling algorithm for the 3-dimensional k -ary torus topology and compare it with established results, both theoretically and experimentally. (This paper is a revised version of Antoine Bossard (2020)).

Sodium-Based Batteries: In Search of the Best Compromise Between Sustainability and Maximization of Electric Performance

Till 2020 the predominant key success factors of battery development have been overwhelmingly energy density, power density, lifetime, safety, and costs per kWh. That is why there is a high expectation on energy storage systems such as lithium-air (Li-O2) and lithium-sulfur (Li-S) systems, especially for mobile applications. These systems have high theoretical specific energy densities compared to conventional Li-ion systems. If the challenges such as practical implementation, low energy efficiency, and cycle life are handled, these systems could provide an interesting energy source for EVs. However, various raw materials are increasingly under critical discussion. Though only 3 wt% of metallic lithium is present in a modern Li-ion cell, absolute high amounts of lithium demand will rise due to the fast-growing market for traction and stationary batteries. Moreover, many lithium sources are not available without compromising environmental aspects. Therefore, there is a growing focus on alternative technologies such as Na-ion and Zn-ion batteries. On a view of Na-ion batteries, especially the combination with carbons derived from food waste as negative electrodes may generate a promising overall cost structure, though energy densities are not as favorable as for Li-ion batteries. Within the scope of this work, the future potential of sodium-based batteries will be discussed in view of sustainability and abundance vs. maximization of electric performance. The major directions of cathode materials development are reviewed and the tendency towards designing high-performance systems is discussed. This paper provides an outlook on the potential of sodium-based batteries in the future battery market of mobile and stationary applications.