The project proposes to define local and global rules for STDP-based learning to evolve compact SNN topologies with less number of neural elements and synaptic connectivity while increasing the application performance. The project proposes to evolve SNN clusters (within the LSM) using unsupervised STDP based algorithms.
Project Description
The project proposes to define local and global rules for STDP-based learning to evolve compact SNN topologies with less number of neural elements and synaptic connectivity while increasing the application performance. The project proposes to evolve SNN clusters (within the LSM) using unsupervised STDP based algorithms. A similar approach with the supervised STDP algorithms will be employed for the feed-forward topology SNN classifiers. The application performance is measured using the training and test data estimation (or classification) accuracy. The proposed algorithms should avoid over-fitting of the SNN to the training dataset while lowering the neural elements and synaptic connectivity.
The target architectural constraints such as neural cluster size and the supported inter- and intra-cluster connectivity also act as inputs to the algorithm. This should result in the evolved clustered SNN application topologies that can be effectively mapped on to the target neuromorphic architecture.
The proposed algorithms should help lower the neural elements and synaptic connectivity in the SNN application topology while increasing the application performance. Lower neural elements and synaptic connectivity directly translates to compact and low power neuromorphic architectures. Studying the evolved SNN application topologies will also help design the efficient synaptic interconnect architecture for neuromorphic architectures.
The project is organised in following phases:
Bachelor's/Master's degree in Electrical/Electronic/Computer Science
Ms Najat Loiazizi, HR Business Partner.
Telephone number: +31 (0)40 40 20 675
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