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Found 5 papers, 0 papers with code
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware.
Device-aware inference operations in SONOS nonvolatile memory arrays
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference.
Evaluating complexity and resilience trade-offs in emerging memory inference machines
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e. g. accuracy continues to scale with parameters and complexity in the face of potential disturbance.
Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication
We present a method for training deep spiking neural networks using an iterative modification of the backpropagation optimization algorithm.
