SiGeSn SBFETs at Cryogenic Temperatures (Tu Wien et al)


A new technical paper titled "A Cryogenic Ultra-Thin Body SiGeSn Transistor" was published by researchers at TU Wien, Johannes Kepler University, Universidad de Granada, and Max Planck Institute for Sustainable Materials. Abstract "Transistors capable of operating at cryogenic temperatures are key components for the fast and energy-efficient control and readout of qubits. However, the ultra... » read more

HW-Accelerated Physical AI Framework For Resource-Constrained Edge Devices (ASU)


A new technical paper titled "Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics" was published by researchers at Arizona State University. Abstract "Physical AI at the edge—enabling autonomous systems to understand and predict real-world dynamics in realtime—demands efficient hardware acceleration. Model recovery (MR), which extracts governing equations ... » read more

Sparse Finite Element Problems on Neuromorphic HW (Sandia National Lab)


A new technical paper titled "Solving sparse finite element problems on neuromorphic hardware" was published by researchers at Sandia National Lab. Abstract "The finite element method (FEM) is one of the most important and ubiquitous numerical methods for solving partial differential equations (PDEs) on computers for scientific and engineering discovery. Applying the FEM to larger and mor... » read more

Thermal-Mechanical Optimization of 2.5D Flip-Chip Packages With Glass and Silicon Interposers (Univ. of Ottawa)


A new technical paper titled "Thermo-mechanical co-design of 2.5D flip-chip packages with silicon and glass interposers via finite element analysis and machine learning" was published by researchers at University of Ottawa. Abstract "Advanced 2.5D flip-chip packages with silicon/glass interposers may pose tightly coupled thermo-mechanical trade-offs. This work presents a simulation-driven, ... » read more

Thermal Scanning-Probe Lithography in vdW Heterostructures (Technical University of Denmark)


A new technical paper titled "Gradient Electronic Landscapes in van der Waals Heterostructures" was published by researchers at Technical University of Denmark. Abstract Excerpt "Here, we use thermal scanning-probe lithography to produce smooth topographic landscapes in vdW heterostructures by patterning the thickness of the top hBN flake with nanometer precision." Find the technical p... » read more

A Review Of Acoustic Side-Channel Attacks: An AI View (Penn State Univ.)


A new technical paper titled "A Survey on Acoustic Side-Channel Attacks: An Artificial Intelligence Perspective" was published by researchers at Penn State University. Abstract "Acoustic Side-Channel Attacks (ASCAs) exploit the sound produced by keyboards and other devices to infer sensitive information without breaching software or network defenses. Recent advances in deep learning, large ... » read more

Impact Of On-Chip SRAM Size And Frequency On Energy Efficiency And Performance of LLM Inference (Uppsala Univ.)


A new technical paper titled "Prefill vs. Decode Bottlenecks: SRAM-Frequency Tradeoffs and the Memory-Bandwidth Ceiling" was published by researchers at Uppsala University. Abstract "Energy consumption dictates the cost and environmental impact of deploying Large Language Models. This paper investigates the impact of on-chip SRAM size and operating frequency on the energy efficiency and per... » read more

Channel-Last GAA NS Oxide FET (Stanford, TSMC, ETH Zurich et al.)


A new technical paper titled "Channel-last gate-all-around nanosheet oxide semiconductor transistors" was published by researchers at Stanford University, TSMC, ETH Zurich, SLAC National Accelerator Laboratory, and Polish Academy of Sciences. Abstract "As we move beyond the era of transistor miniaturization, back-end-of-line-compatible transistors that can be stacked monolithically in the t... » read more

DL Atomistic Semi-Empirical Pseudopotential Model For Nanomaterials (UC Berkeley, LBNL et al.)


A new technical paper titled "Deep-learning atomistic semi-empirical pseudopotential model for nanomaterials" was published by researchers at UC Berkeley, Lawrence Berkeley National Laboratory et al. Abstract "The semi-empirical pseudopotential method (SEPM) has been widely applied to provide computational insights into the electronic structure, photophysics, and charge carrier dynamics of ... » read more

Study Of HW Acceleration for Neural Networks (Arizona State Univ.)


A new technical paper titled "Hardware Acceleration for Neural Networks: A Comprehensive Survey" was published by researchers at Arizona State University. Abstract "Neural networks have become a dominant computational workload across cloud and edge platforms, but their rapid growth in model size and deployment diversity has exposed hardware bottlenecks that are increasingly dominated by mem... » read more

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