Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data


Abstract "In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles. There is a bro... » read more

Quantum Machine Learning: Security Threats & Lines Of Defense


New research paper from Pennsylvania State University explores quantum machine learning (QML) and its use in hardware security. Find the technical paper here. April 2022. Satwik Kundu and Swaroop Ghosh. 2022. Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses (Invited). In Proceedings of the Great Lakes Symposium on VLSI 2022 (GLSVLSI ’22), June 6–8,... » read more

Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems


New academic paper from Washington State University, supported by a grant from the National Science Foundation. Abstract: "Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP),... » read more

Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics


New academic paper out of USC Viterbi School of Engineering: Abstract "Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales w... » read more

Neuromorphic chip integrated with a large-scale integration circuit and amorphous-metal-oxide semiconductor thin-film synapse devices


New academic paper from Nara Institute of Science and Technology (NAIST) and Ryukoku University. Abstract "Artificial intelligences are promising in future societies, and neural networks are typical technologies with the advantages such as self-organization, self-learning, parallel distributed computing, and fault tolerance, but their size and power consumption are large. Neuromorphic syste... » read more

Nonvolatile Capacitive Crossbar Array for In-Memory Computing


Abstract "Conventional resistive crossbar array for in-memory computing suffers from high static current/power, serious IR drop, and sneak paths. In contrast, the “capacitive” crossbar array that harnesses transient current and charge transfer is gaining attention as it 1) only consumes dynamic power, 2) has no DC sneak paths and avoids severe IR drop (thus, selector-free), and 3) can be f... » read more

An adaptive synaptic array using Fowler–Nordheim dynamic analog memory


Abstract "In this paper we present an adaptive synaptic array that can be used to improve the energy-efficiency of training machine learning (ML) systems. The synaptic array comprises of an ensemble of analog memory elements, each of which is a micro-scale dynamical system in its own right, storing information in its temporal state trajectory. The state trajectories are then modulated by a sys... » read more

OverlapNet: Loop Closing for LiDAR-based SLAM


Abstract: "Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data for finding loop closures. It estimates an image overlap gene... » read more

Improving Machine Learning-Based Modeling of Semiconductor Devices by Data Self-Augmentation


Abstract: "In the electronics industry, introducing Machine Learning (ML)-based techniques can enhance Technology Computer-Aided Design (TCAD) methods. However, the performance of ML models is highly dependent on their training datasets. Particularly in the semiconductor industry, given the fact that the fabrication process of semiconductor devices is complicated and expensive, it is of grea... » read more

Rotating neurons for all-analog implementation of cyclic reservoir computing


Abstract "Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed. Here, we propose a versatile method for implemen... » read more

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