[HTML][HTML] MA2Z4 family heterostructures: Promises and prospects
Recent experimental synthesis of ambient-stable MoSi 2 N 4 monolayer has garnered
enormous research interest. The intercalation morphology of MoSi 2 N 4—composed of a …
enormous research interest. The intercalation morphology of MoSi 2 N 4—composed of a …
WaSSaBi: Wafer Selection With Self-Supervised Representations and Brain-Inspired Active Learning
K Pandaram, PR Genssler… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Large datasets are often available for machine learning tasks. However, only very few
contain labels for all the samples because labeling is a very labor-intensive process. Hence …
contain labels for all the samples because labeling is a very labor-intensive process. Hence …
Hyperdimensional computing for resilient edge learning
Recent strides in deep learning have yielded impres-sive practical applications such as
autonomous driving, natural language processing, and graph reasoning. However, the sus …
autonomous driving, natural language processing, and graph reasoning. However, the sus …
HDCircuit: Brain-inspired hyperdimensional computing for circuit recognition
Circuits possess a non-Euclidean representation, necessitating the encoding of their data
structure (eg, gate-level netlists) into fixed formats like vectors. This work is the first to …
structure (eg, gate-level netlists) into fixed formats like vectors. This work is the first to …
Tutorial: The Synergy of Hyperdimensional and In-memory Computing
Breakthroughs in deep learning consistently drive innovation. However, DNNs tend to
overwhelm conventional computing systems. Hyperdimensional Computing (HDC) is rapidly …
overwhelm conventional computing systems. Hyperdimensional Computing (HDC) is rapidly …
Beyond von Neumann era: brain-inspired hyperdimensional computing to the rescue
Breakthroughs in deep learning (DL) continuously fuel innovations that profoundly improve
our daily life. However, DNNs overwhelm conventional computing architectures by their …
our daily life. However, DNNs overwhelm conventional computing architectures by their …
CircuitHD: Brain-inspired Hyperdimensional Computing for Circuit Recognition
Circuits possess an irregular representation. Hence, their data structure (eg, gate-level
netlists) necessitate the encoding into fixed formats like vectors. For machine learning-based …
netlists) necessitate the encoding into fixed formats like vectors. For machine learning-based …
Machine Learning Methodologies to Predict the Results of Simulation-Based Fault Injection
Simulation-based fault injection is a widely used technique for early-stage circuit reliability
analysis. However, it consumes significant time, particularly for complex circuits. This paper …
analysis. However, it consumes significant time, particularly for complex circuits. This paper …
Accelerate SEU Simulation-Based Fault Injection With Spatio-Temporal Graph Convolutional Networks
L Lu, J Chen, A Balakrishnan… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Evaluating the sensitivity of circuits to Single Event Upset (SEU) faults has become
increasingly important and challenging due to the growing complexity of circuits. Simulation …
increasingly important and challenging due to the growing complexity of circuits. Simulation …
Frontiers in Edge AI with RISC-V: Hyperdimensional Computing vs. Quantized Neural Networks
PR Genssler, SA Wasif, M Wael… - … , Automation & Test …, 2024 - ieeexplore.ieee.org
Hyperdimensional Computing (HDC) is an emerging paradigm that stands as a compelling
alternative to conventional Deep Learning algorithms. HDC holds four key promises. First …
alternative to conventional Deep Learning algorithms. HDC holds four key promises. First …