3 citations · 3 across the 4 of their papers we have counts for
4 papers
Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA
Kamil Jeziorek, Piotr Wzorek, Krzysztof Blachut +6
As the volume of data recorded by embedded edge sensors increases, particularly from neuromorphic devices producing discrete event streams, there is a growing need for hardware-awa…
Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA
Hiroshi Nakano, Krzysztof Blachut, Kamil Jeziorek +6
As the quantities of data recorded by embedded edge sensors grow, so too does the need for intelligent local processing. Such data often comes in the form of time-series signals, b…
Scaling-up Memristor Monte Carlo with magnetic domain-wall physics
Thomas Dalgaty, Shogo Yamada, Anca Molnos +8
By exploiting the intrinsic random nature of nanoscale devices, Memristor Monte Carlo (MMC) is a promising enabler of edge learning systems. However, due to multiple algorithmic an…
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving
Sambit Mohapatra, Thomas Mesquida, Mona Hodaei +3
Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latenc…