A5010442001- Advanced Memory and Neural Computing
- Traffic Prediction and Management Techniques
- CCD and CMOS Imaging Sensors
- Risk and Safety Analysis
- Neural Networks and Applications
- Advanced Semiconductor Detectors and Materials
- Fire Detection and Safety Systems
- Neural Networks and Reservoir Computing
- Ferroelectric and Negative Capacitance Devices
- Neural dynamics and brain function
University of Tennessee System
2023
University of Tennessee at Knoxville
2023
Atkins (United Kingdom)
2002
Cryogenic neuromorphic systems, inspired by the brains unparalleled efficiency, present a promising paradigm for next generation computing architectures.This work introduces fully integrated framework that combines superconducting memristor(SM) based spiking neurons and synapse topologies to achieve low power network with non volatile synaptic strength.This neurosynaptic is validated implementing cart pole control task, dynamic decision making problem requiring real time computation.Through...
Abstract Neuromorphic computing is a novel style of that features low-power spiking neural networks as the main compute components. It an event-driven computational paradigm naturally pairs with event-based cameras and their asynchronous event output. In this work, we present NeuroPong, closed-loop neuromorphic hardware system composed camera, system, Atari 2600 console. The facilitates implementation network agents capable playing games in real time using camera capture input. We perform...
The work of monitoring dangerous goods which is being undertaken within the CEC's Advanced Transport Telematics Program discussed, and FRAME (Freight Management in Europe) described. project undertook a major analysis user requirements, clearly defined these, then proceeded with functional system design. legislative framework, commercial benefit cost, institutional support are factors involved implementation any architecture used to monitor goods. described cognizant all these factors.
Neurons that fire multiple spikes on activation are commonly observed in biological systems, but the impact of their inclusion neuromorphic systems has not been thoroughly analyzed. In this preliminary work, we begin an initial evaluation multi-fire neurons classification and control task performance. We show networks with these evolved for tasks tend to perform worse than single-fire neurons; however, also significantly better when included reservoir computing approaches tasks.