Microsoft Research’s Post

The future of compute hardware for AI is more uncertain than ever. How should AI models change if compute hardware changes? What sort of models could be build with different, for example, analog or neuromorphic hardware? How should we train models that run on analog or photonic chips? How can models deal with noise or very narrow data types? We will discuss this and more at our NeurIPS 2024 workshop in Vancouver! Call for Papers: ML with New Compute Paradigms (MLNCP) Workshop at NeurIPS 2024 We call for workshop papers including but not limited to the following directions: ·       Advances in machine-learning that benefit from compute paradigms beyond standard digital compute, for example analog, photonic, in-memory, neuromorphic, or quantum compute. ·       Advances in machine learning methods that can handle challenges such as low bit precision, variability or noise induced by new hardware. Examples include inherently noise-robust models, algorithms to reduce errors in ML arithmetic, or ways to share and distribute models across unique analog hardware. ·       Advances in machine-learning paradigms that facilitate training and/or inference on new hardware paradigms. This can be general or specific to, for example, generative models for modalities such as image or sequence data. ·       Surveys and position papers for machine-learning with new compute paradigms. The submission deadline is Aug 29 '24 (Anywhere on Earth)  Please check the workshop website for updates on format and possible deadline extensions. Workshop Website: www.mlwithnewcompute.com