Audio-Based Identification of Queen Bee Presence Inside Beehives

Honeybees are essential for the health of people and the planet. They play a key role in the pollination of most crops. The high mortality observed in the last decade, caused by stress factors among which the climate change, have raised the necessity of remote sensing the beehives to help monitor the health of honeybees and better understand this phenomenon. Several solutions have been proposed in the literature, and some of them include the analysis of in-hive sounds. In this scenario, we explore the potential of machine learning methods for queen bee detection using only the audio signal, being a good indicator of the colony state of health. In particular, we experiment support vector machines and neural network classifiers. We consider the effect of varying the audio chunk duration and the adoption of different hyperparameters

New Logic-In-Memory Paradigms: An Architectural and Technological Perspective

Processing systems are in continuous evolution thanks to the constant technological advancement and architectural progress. Over the years, computing systems have become more and more powerful, providing support for applications, such as Machine Learning, that require high computational power. However, the growing complexity of modern computing units and applications has had a strong impact on power consumption. In addition, the memory plays a key role on the overall power consumption of the system, especially when considering data-intensive applications. These applications, in fact, require a lot of data movement between the memory and the computing unit. The consequence is twofold: Memory accesses are expensive in terms of energy and a lot of time is wasted in accessing the memory, rather than processing, because of the performance gap that exists between memories and processing units. This gap is known as the memory wall or the von Neumann bottleneck and is due to the different rate of progress between complementary metal-oxide semiconductor (CMOS) technology and memories. However, CMOS scaling is also reaching a limit where it would not be possible to make further progress. This work addresses all these problems from an architectural and technological point of view by: (1) Proposing a novel Configurable Logic-in-Memory Architecture that exploits the in-memory computing paradigm to reduce the memory wall problem while also providing high performance thanks to its flexibility and parallelism; (2) exploring a non-CMOS technology as possible candidate technology for the Logic-in-Memory paradigm

ToPoliNano: Nanoarchitectures Design Made Real

Many facts about emerging nanotechnologies are yet to be assessed. There are still major concerns, for instance, about maximum achievable device density, or about which architecture is best fit for a specific application. Growing complexity requires taking into account many aspects of technology, application and architecture at the same time. Researchers face problems that are not new per se, but are now subject to very different constraints, that need to be captured by design tools. Among the emerging nanotechnologies, two-dimensional nanowire based arrays represent promising nanostructures, especially for massively parallel computing architectures. Few attempts have been done, aimed at giving the possibility to explore architectural solutions, deriving information from extensive and reliable nanoarray characterization. Moreover, in the nanotechnology arena there is still not a clear winner, so it is important to be able to target different technologies, not to miss the next big thing. We present a tool, ToPoliNano, that enables such a multi-technological characterization in terms of logic behavior, power and timing performance, area and layout constraints, on the basis of specific technological and topological descriptions. This tool can aid the design process, beside providing a comprehensive simulation framework for DC and timing simulations, and detailed power analysis. Design and simulation results will be shown for nanoarray-based circuits. ToPoliNano is the first real design tool that tackles the top down design of a circuit based on emerging technologie

Parallel Computation in the Racetrack Memory

Racetrack memories are promising candidates for next-generation solid-state storage devices. Various racetrack memories have been proposed in the literature, skyrmion based or domain wall based. However, none of them show integrated computing capabilities. Here, we introduce a new domain wall based racetrack concept that can operate both as a memory and as a computing device. The computation is defined by changing locally the anisotropy of the film. Stray fields from nearby cells are exploited to implement reconfigurable logic gates. We demonstrate that the racetrack array can operate in parallel in every cell. This is achieved by an external out-of-plane Zeeman field applied to the array. As proof-of-principle, we verified the single computing cell and multiple connected cells operating in parallel by micromagnetic simulations. Logic NAND/NOR is implemented independently in every computing cell. This study provides the guidelines for the development and optimization of this family of logic gates

Custom Memory Design for Logic-in-Memory: Drawbacks and Improvements over Conventional Memories

The speed of modern digital systems is severely limited by memory latency (the “Memory Wall” problem). Data exchange between Logic and Memory is also responsible for a large part of the system energy consumption. Logic-in-Memory (LiM) represents an attractive solution to this problem. By performing part of the computations directly inside the memory the system speed can be improved while reducing its energy consumption. LiM solutions that offer the major boost in performance are based on the modification of the memory cell. However, what is the cost of such modifications? How do these impact the memory array performance? In this work, this question is addressed by analysing a LiM memory array implementing an algorithm for the maximum/minimum value computation. The memory array is designed at physical level using the FreePDK 45nm CMOS process, with three memory cell variants, and its performance is compared to SRAM and CAM memories. Results highlight that read and write operations performance is worsened but in-memory operations result to be very efficient: a 55.26% reduction in the energy-delay product is measured for the AND operation with respect to the SRAM read one. Therefore, the LiM approach represents a very promising solution for low-density and high-performance memories

Device for Data Storage and Processing, and Method Thereof

A device for data storage and processing includes: at least two input racetrack elements having a plurality of first magnetization regions; at least one output racetrack element having a plurality of second magnetization regions, wherein a magnetization vector is adapted to switch from a first direction to the opposite one, or vice versa, by way of a magnetic field of reduced intensity compared with a magnetic field required to produce a similar switching of a magnetization vector of the first magnetization region, wherein the input racetrack elements and output racetrack element are configured in such a way as to constitute at least one elementary logic gate, wherein at least two of the first magnetization regions are magnetically coupled to at least one of the second magnetization regions

Integration of Simulated Quantum Annealing in Parallel Tempering and Population Annealing for Heterogeneous-Profile QUBO Exploration

Simulated Quantum Annealing (SQA) is a heuristic algorithm which can solve Quadratic Unconstrained Binary Optimization (QUBO) problems by emulating the exploration of the solution space done by a quantum annealer. It mimics the quantum superposition and tunnelling effects through a set of correlated replicas of the spins system representing the problem to be solved and performing Monte Carlo steps. However, the effectiveness of SQA over a classical algorithm strictly depends on the cost/energy profile of the target problem. In fact, quantum annealing only performs well in exploring functions with high and narrow peaks, while classical annealing is better in overcoming flat and wide energy-profile barriers. Unfortunately, real-world problems have a heterogeneous solution space and the probability of success of each solver depends on the size of the energy profile region compatible with its exploration mechanism. Therefore, significant advantages could be obtained by exploiting hybrid solvers, which combine SQA and classical algorithms. This work proposes four new quantum-classical algorithms: Simulated Quantum Parallel Tempering (SQPT), Simulated Quantum Population Annealing (SQPA), Simulated Quantum Parallel Tempering - Population Annealing v1 (SQPTPA1) and Simulated Quantum Parallel Tempering - Population Annealing v2 (SQPTPA2). They are obtained by combining SQA, Parallel Tempering (PT), and Population Annealing (PA). Their results are compared with those provided by SQA, considering benchmark QUBO problems, characterized by different profiles. Even though this work is preliminary, the obtained results are encouraging and prove hybrid solvers’ potential in solving a generic optimization problem

An Integrated Multi-Sensor System for Remote Bee Health Monitoring

Over 75% of the world's food crops depends on pollination and in particular by the inestimable value of the service provided by bees. Besides, the bee colony health is a good indicator of the quality of the environment and it is strongly affected by many aspects such as beekeepers' management practices, policies adopted for cropping and land use. However, the climate change, the intensive agriculture, pesticides, biodiversity loss, Varroa mites and pollution are the leading cause of bees death world wide. The role of beekeepers is of extremely importance to mitigate this damage. Apiaries are usually located in remote environment an require frequent visit by the beekeepers. Indeed, the beekeeping sector lacks of suitable tools for risk assessment and decision making that can be used by stakeholders. Smart monitoring systems assessing the health of the colony and the honey production would be beneficial for such community. In this work, we present a prototype of an embedded multi-sensor system for beehive monitoring with the aim of providing a simple solution to beekeepers. Indeed, the proposed system do not require modification of the beehive and it is compact enough to be simply inserted in the brood box. It measures the vital parameters of the beehive, such as temperature, weight, humidity and CO2 concentration. It exploits the low power communication protocol LoRaWAN for the data transmission. The collected data are made available to the beekeeper through a web application. We show the effectiveness of such compact, non-invasive embedded system with its installation in an apiary

SCERPA: a Self-Consistent Algorithm for the Evaluation of the Information Propagation in Molecular Field-Coupled Nanocomputing

Among the emerging technologies that are intended to outperform the current CMOS technology, the field-coupled nanocomputing (FCN) paradigm is one of the most promising. The molecular quantum-dot cellular automata (MQCA) has been proposed as possible FCN implementation for the expected very high device density and possible room temperature operations. The digital computation is performed via electrostatic interactions among nearby molecular cells, without the need for charge transport, extremely reducing the power dissipation. Due to the lack of mature analysis and design methods, especially from an electronics standpoint, few attempts have been made to study the behavior of logic circuits based on real molecules, and this reduces the design capability. In this article, we propose a novel algorithm, named self-consistent electrostatic potential algorithm (SCERPA), dedicated to the analysis of molecular FCN circuits. The algorithm evaluates the interaction among all molecules in the system using an iterative procedure. It exploits two optimizations modes named Interaction Radius and Active Region which reduce the computational cost of the evaluation, enabling SCERPA to support the simulation of complex molecular FCN circuits and to characterize consequentially the technology potentials. The proposed algorithm fulfills the need for modeling the molecular structures as electronic devices and provides important quantitative results to analyze the information propagation, motivating and supporting further research regarding molecular FCN circuits and eventual prototype fabrication