Fabio Carta
YOU?
Author Swipe
View article: Experimental Investigation on the Effects of Air-Lubrication in a Stepped Planing Hull
Experimental Investigation on the Effects of Air-Lubrication in a Stepped Planing Hull Open
In the constant search for new methods to reduce the resistance of ships and, consequently, overall emissions, active boundary layer ventilation through air injection is currently one of the most effective techniques. While its application…
View article: BPS spectroscopy with reinforcement learning
BPS spectroscopy with reinforcement learning Open
We apply reinforcement learning (RL) to establish whether at a given position in the Coulomb branch of the moduli space of a 4d $\mathcal{N} = 2$ quantum field theory (QFT) the BPS spectrum is finite. If it is, we furthermore determine the…
View article: Demonstration of transfer learning using 14 nm technology analog ReRAM array
Demonstration of transfer learning using 14 nm technology analog ReRAM array Open
Analog memory presents a promising solution in the face of the growing demand for energy-efficient artificial intelligence (AI) at the edge. In this study, we demonstrate efficient deep neural network transfer learning utilizing hardware a…
View article: Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm
Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm Open
A critical bottleneck for the training of large neural networks (NNs) is communication with off-chip memory. A promising mitigation effort consists of integrating crossbar arrays of analogue memories in the Back-End-Of-Line, to store the N…
View article: Using the IBM analog in-memory hardware acceleration kit for neural network training and inference
Using the IBM analog in-memory hardware acceleration kit for neural network training and inference Open
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripher…
View article: Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference Open
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal periphe…
View article: Fast offset corrected in-memory training
Fast offset corrected in-memory training Open
In-memory computing with resistive crossbar arrays has been suggested to accelerate deep-learning workloads in highly efficient manner. To unleash the full potential of in-memory computing, it is desirable to accelerate the training as wel…
View article: A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays
A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays Open
We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PyTorch (freely available at https://github.com/IBM/aihwkit). The t…