Progress in artificial intelligence and machine learning over the past d...
Transformers have achieved great success in a wide variety of natural
la...
Approximate computing (AxC) has been long accepted as a design alternati...
Deep Learning neural networks are pervasive, but traditional computer
ar...
We propose 2D Piezoelectric FET (PeFET) based compute-enabled non-volati...
We propose non-volatile memory (NVM) designs based on Piezoelectric Stra...
Deep Neural Networks (DNNs) have transformed the field of machine learni...
Graph Neural Networks (GNNs) use a fully-connected layer to extract feat...
Transformers have transformed the field of natural language processing. ...
Implantable and wearable medical devices (IWMDs) are widely used for the...
Precision scaling has emerged as a popular technique to optimize the com...
Transformer models have garnered a lot of interest in recent years by
de...
Adversarial attacks have exposed serious vulnerabilities in Deep Neural
...
Ensuring robustness of Deep Neural Networks (DNNs) is crucial to their
a...
Modern deep networks have millions to billions of parameters, which lead...
Resistive crossbars have attracted significant interest in the design of...
The enormous inference cost of deep neural networks can be scaled down b...
The use of lower precision has emerged as a popular technique to optimiz...
Object detection in videos is an important task in computer vision for
v...
Deep Neural Networks (DNNs) are widely used to perform machine learning ...
Deep Neural Networks (DNNs) have emerged as the method of choice for sol...
Deep Neural Networks (DNNs) have advanced the state-of-the-art in a vari...
Large-scale artificial neural networks have shown significant promise in...
Spin Transfer Torque MRAMs are attractive due to their non-volatility, h...