Search Results for author:
Found 5 papers, 0 papers with code
Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits
The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match.
Demonstration of Programmable Brain-Inspired Optoelectronic Neuron in Photonic Spiking Neural Network with Neural Heterogeneity
Photonic Spiking Neural Networks (PSNN) composed of the co-integrated CMOS and photonic elements can offer low loss, low power, highly-parallel, and high-throughput computing for brain-inspired neuromorphic systems.
Scalable Nanophotonic-Electronic Spiking Neural Networks
Photonic devices are ideal for the design of high-bandwidth, parallel architectures matching the SNN computational paradigm.
Izhikevich-Inspired Optoelectronic Neurons with Excitatory and Inhibitory Inputs for Energy-Efficient Photonic Spiking Neural Networks
The optoelectronic neurons consist of three transistors acting as electrical spiking circuits, a vertical-cavity surface-emitting laser (VCSEL) for optical spiking outputs, and two photodetectors for excitatory and inhibitory optical spiking inputs.
DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks
To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i. e., DeepRMSA-FLX.
