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The advent of devices based on single dopants, such as the single atom transistor, the single spin magnetometer and the single atom memory, motivates the quest for strategies that permit to control matter with atomic precision. Manipulation…
We perform numerical magnetohydrodynamic (MHD) simulations of the gravitational collapse and fragmentation of a cylindrical molecular cloud with the help of the FLASH code. The cloud collapses rapidly along its radius without any signs of…
Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmark datasets. Here, we investigate the performance of machine…
One of the main, long-term objectives of artificial intelligence is the creation of thinking machines. To that end, substantial effort has been placed into designing cognitive systems; i.e. systems that can manipulate semantic-level…
Memory-hard functions (MHF) are functions whose evaluation cost is dominated by memory cost. MHFs are egalitarian, in the sense that evaluating them on dedicated hardware (like FPGAs or ASICs) is not much cheaper than on off-the-shelf…
Hyperdimensional computing (HDC) is emerging as a promising AI approach that can effectively target TinyML applications thanks to its lightweight computing and memory requirements. Previous works on HDC showed that limiting the standard 10k…
To address the computational and storage challenges posed by large-scale datasets in deep learning, dataset distillation has been proposed to synthesize a compact dataset that replaces the original while maintaining comparable model…
Dynamic random access memory (DRAM) is critical to classical computing but notably absent in current superconducting quantum processors. Integrating high-coherence memory units would enable resource-efficient control of logical qubits and…
Quantum memory plays a pivotal role in the construction of quantum repeaters, which are essential devices for establishing long-distance quantum communications and large-scale quantum networks. To boost information capacity and signal…
One requirement of maintaining digital information is storage. With the latest advances in the digital world, new emerging media types have required even more storage space to be kept than before. In fact, in many cases it is required to…
Microwave quantum memory promises advanced capabilities for noisy intermediate-scale superconducting quantum computers. Existing approaches to microwave quantum memory lack complete combination of high efficiency, long storage time,…
The Last Level Cache (LLC) is the processor's critical bridge between on-chip and off-chip memory levels - optimized for high density, high bandwidth, and low operation energy. To date, high-density (HD) SRAM has been the conventional…
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms…
We present GridFF, an efficient method for simulating molecules on rigid substrates, derived from techniques used in protein-ligand docking in biochemistry. By projecting molecule-substrate interactions onto precomputed spatial grids with…
We live in a data-centric world where we are heading to generate close to 200 Zettabytes of data by the year 2025. Our data processing requirements have also increased as we push to build data processing frameworks that can process large…
Dissipative cognitive architectures maintain computation through continuous energy expenditure, where units that exhaust their energy are stochastically replaced with fresh random state. This creates a fundamental challenge: how can…
Systolic arrays and shared-L1-memory manycore clusters are commonly used architectural paradigms that offer different trade-offs to accelerate parallel workloads. While the first excel with regular dataflow at the cost of rigid…
Understanding the physical and chemical mechanisms occurring during the forming process and operation of an organic resistive memory device is a major issue for better performances. Various mechanisms were suggested in vertically stacked…
Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance…
For the practical implementation of massive multiple-input multiple-output (MIMO) systems, the hybrid processing (precoding/combining) structure is promising to reduce the high cost rendered by large number of RF chains of the traditional…