In this paper, we introduce a new perspective on training deep neural networks capable of
state-of-the-art performance without the need for the expensive over-parameterization by …

In-memory computing (IMC) addresses the cost of accessing data from memory in a manner
that introduces a tradeoff between energy/throughput and computation signal-to-noise ratio …

In recent years, the fields of natural language processing (NLP) and information retrieval (IR)
have made tremendous progress thanks to deep learning models like Recurrent Neural …

The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime
based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been …

Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for
applications with time-series and sequential data. Recently, there has been a strong interest …

This work demonstrates a programmable in-memory-computing (IMC) inference accelerator
for scalable execution of neural network (NN) models, leveraging a high-signal-to-noise …