Search Results for author:
Found 4 papers, 1 papers with code
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
Despite a well-established mathematical foundation for neural dynamics, the implementation details vary greatly across different platforms.
Concepts and Paradigms for Neuromorphic Programming
The value of neuromorphic computers depends crucially on our ability to program them for relevant tasks.
Training a spiking neural network on an event-based label-free flow cytometry dataset
Imaging flow cytometry systems aim to analyze a huge number of cells or micro-particles based on their physical characteristics.
Automated Architecture Design for Deep Neural Networks
This thesis inspects different approaches, existing and novel, to automate the design of deep feedforward neural networks in an attempt to create less complex models with good performance that take away the burden of deciding on an architecture and make it more efficient to design and train such deep networks.
