Ferroelectric materials, the charge equivalent of magnets, have been the subject of
continued research interest since their discovery more than 100 years ago. The …

Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

To efficiently deploy machine learning applications to the edge, compute-in-memory (CIM)
based hardware accelerator is a promising solution with improved throughput and energy …

Costly data movement in terms of time and energy in traditional von Neumann systems is
exacerbated by emerging information technologies related to artificial intelligence. In …

Focus in deep neural network hardware research for reducing latencies of memory fetches
has steered in the direction of analog-based artificial neural networks (ANN). The promise of …

Graph neural network (GNN) is a variant of deep neural networks (DNNs) operating on
graphs. However, GNNs are more complex compared with DNNs as they simultaneously …

After the success of the transformer networks on natural language processing (NLP), the
application of transformers to computer vision (CV) has followed suit to deliver …

Voltages and currents in a memristor crossbar can be significantly affected due to nonideal
effects such as parasitic source, line, and neuron resistance. These nonideal effects related …

In the internet-of-things era, edge intelligence is critical for overcoming the communication
and computing energy crisis, which is unavoidable if cloud computing is used exclusively …

Memristor crossbars can be very useful for realizing edge-intelligence hardware, because
the neural networks implemented by memristor crossbars can save significantly more …