Energy-efficient matrix-vector multiplications (MVMs) are key to bringing neural network
(NN) inference to edge devices. This has led to a wide range of state-of-the-art MVM …

Deep learning constitutes the state-of-the-art in machine learning, from data mining to
computer vision and natural language processing. Energy-efficient matrix-vector …

This article studies the merits of applying log-gradient input images to convolutional neural
networks (CNNs) for tinyML computer vision (CV). We show that log gradients enable:(i) …

We propose, for the first time, an indium gallium zinc oxide (IGZO)-based 2T1C compute cell
(IGZO-cell) for analog in-memory computing. To assess the impact of an IGZO-cell-based …

Ternary Neural Networks (TNNs) and mixed-precision Ternary Binary Networks (TBNs) have
demonstrated higher accuracy compared to Binary Neural Networks (BNNs) while providing …

This paper studies the merits of applying log-gradient input images to convolutional neural
networks (CNNs) for tinyML computer vision (CV). We show that log gradients enable:(i) …

Analog in-memory compute (AiMC) is a promising approach to efficiently process deep
neural networks (DNNs). Due to its small size and nonvolatility, oxide resistive random …

Extremely energy-efficient convolutional neural network inference (CNN) is recently enabled
by analog in-memory compute (AiMC). The integration of AiMC in a primarily digital …

The need to execute Deep Neural Networks (DNNs) at low latency and low power at the
edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) …

A rising research challenge is running costly machine learning (ML) networks locally on
resource-constrained edge devices. ML networks with large convolutional layers can easily …