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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)…
The development of new computing technologies has given a new stimulus in the study of multiferroics. The use of multiferroics allows the realization of competitive energy efficient scalable logic and storage devices. The low-power…
Magnon transistors that can effectively regulate magnon transport by an electric field are desired for magnonics which aims to provide a Joule-heating free alternative to the conventional electronics owing to the electric neutrality of…
The balance between low power consumption and high efficiency in memory devices is a major limiting factor in the development of new technologies. Magnetic random access memories (MRAM) based on CoFeB/MgO magnetic tunnel junctions (MTJs)…
We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and…
Current Artificial Intelligence (AI) computation systems face challenges, primarily from the memory-wall issue, limiting overall system-level performance, especially for Edge devices with constrained battery budgets, such as smartphones,…
The speed of writing of state-of-the-art ferromagnetic memories is physically limited by an intrinsic GHz threshold. Recently, an alternative research direction has been initiated by realizing memory devices based on antiferromagnets in…
Processing-in-memory (PIM) is attractive to overcome the limitations of modern computing systems. Numerous PIM systems exist, varying by the technologies and logic techniques used. Successful operation of specific logic functions is crucial…
Compute-in-memory (CIM) accelerators for spiking neural networks (SNNs) are promising solutions to enable $\mu$s-level inference latency and ultra-low energy in edge vision applications. Yet, their current lack of flexibility at both the…
Multiferroics have found renewed interest in topological magnetism and for logic-in-memory applications. Among them, SrMnO$_{3}$, possessing strong magnetoelectric coupling is gaining attention for the design of coexisting magnetic and…
As transistor-based memory technologies like dynamic random access memory (DRAM) approach their scalability limits, the need to explore alternative storage solutions becomes increasingly urgent. Phase-change memory (PCM) has gained…
Integrated circuits are a cornerstone of modern information technology, and analog wave-based architectures could enable fast and efficient processing beyond conventional charge electronics. In magnonics, spin waves provide a highly…
In the era of artificial intelligence (AI), Transformer demonstrates its performance across various applications. The excessive amount of parameters incurs high latency and energy overhead when processed in the von Neumann architecture.…
As dynamic random access memory (DRAM) and other current transistor-based memories approach their scalability limits, the search for alternative storage methods becomes increasingly urgent. Phase-change memory (PCM) emerges as a promising…
Reservoir computing offers an energy-efficient alternative to deep neural networks (DNNs) by replacing complex hidden layers with a fixed nonlinear system and training only the final layer. This work investigates nanoelectromechanical…
In this work, we propose valley-coupled spin-hall memories (VSH-MRAMs) based on monolayer WSe2. The key features of the proposed memories are (a) the ability to switch magnets with perpendicular magnetic anisotropy (PMA) via VSH effect and…
Fine-grained sparsity promises higher parametric capacity without proportional per-token compute, but often suffers from training instability, load balancing, and communication overhead. We introduce STEM (Scaling Transformers with…
The inherent dynamics of the neuron membrane potential in Spiking Neural Networks (SNNs) allows processing of sequential learning tasks, avoiding the complexity of recurrent neural networks. The highly-sparse spike-based computations in…
With ultra-fast writing capacity and high reliability, the spin-orbit torque is regarded as a promising alternative to fabricate next-generation magnetic random access memory. However, the three-terminal setup can be challenging when…
While most neuromorphic systems are based on nanoscale electronic devices, nature relies on ions for energy-efficient information processing. Therefore, finding memristive nanofluidic devices is a milestone toward realizing electrolytic…