Implementing spiking neural networks on neuromorphic architectures: A review
Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …
systems to execute machine learning applications that are designed using Spiking Neural …
Evolutionary optimization for neuromorphic systems
CD Schuman, JP Mitchell, RM Patton… - Proceedings of the …, 2020 - dl.acm.org
Designing and training an appropriate spiking neural network for neuromorphic deployment
remains an open challenge in neuromorphic computing. In 2016, we introduced an …
remains an open challenge in neuromorphic computing. In 2016, we introduced an …
Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge
CMOS-RRAM integration holds great promise for low energy and high throughput
neuromorphic computing. However, most RRAM technologies relying on filamentary …
neuromorphic computing. However, most RRAM technologies relying on filamentary …
Bayesian multi-objective hyperparameter optimization for accurate, fast, and efficient neural network accelerator design
In resource-constrained environments, such as low-power edge devices and smart sensors,
deploying a fast, compact, and accurate intelligent system with minimum energy is …
deploying a fast, compact, and accurate intelligent system with minimum energy is …
Non-traditional input encoding schemes for spiking neuromorphic systems
A key challenge for utilizing spiking neural networks or spiking neuromorphic systems for
most applications is translating numerical data into spikes that are appropriate to apply as …
most applications is translating numerical data into spikes that are appropriate to apply as …
On-sensor data filtering using neuromorphic computing for high energy physics experiments
This work describes the investigation of neuromorphic computing-based spiking neural
network (SNN) models used to filter data from sensor electronics in high energy physics …
network (SNN) models used to filter data from sensor electronics in high energy physics …
Recent Trends in Application of Memristor in Neuromorphic Computing: A Review
S Panda, C Sekhar Dash, C Dora - Current Nanoscience, 2024 - ingentaconnect.com
Recently memristors have emerged as a form of nonvolatile memory that is based on the
principle of ion transport in solid electrolytes under the impact of an external electric field. It …
principle of ion transport in solid electrolytes under the impact of an external electric field. It …
Benchmarking the performance of neuromorphic and spiking neural network simulators
Software simulators play a critical role in the development of new algorithms and system
architectures in any field of engineering. Neuromorphic computing, which has shown …
architectures in any field of engineering. Neuromorphic computing, which has shown …
Evolutionary vs imitation learning for neuromorphic control at the edge
C Schuman, R Patton, S Kulkarni… - Neuromorphic …, 2022 - iopscience.iop.org
Neuromorphic computing offers the opportunity to implement extremely low power artificial
intelligence at the edge. Control applications, such as autonomous vehicles and robotics …
intelligence at the edge. Control applications, such as autonomous vehicles and robotics …
Evaluating encoding and decoding approaches for spiking neuromorphic systems
A challenge associated with effectively using spiking neuromorphic systems is how to
communicate data to and from the neuromorphic implementation. Unless a neuromorphic or …
communicate data to and from the neuromorphic implementation. Unless a neuromorphic or …