Welcome to the MLNCP Workshop at NeurIPS 2023!
This workshop aims to bring together ML researchers with academic and industrial researchers building novel AI accelerators. The goal is to enable interaction between the two groups and kick-start a new feedback cycle between models and accelerators and to enable hardware-model co-design. We welcome relevant algorithmic or model-innovations as well as results demonstrated on accelerators in the following categories:
- photonic or optical compute
- neuromorphic compute
- in-memory compute
- low-precision and edge-compute
- analog compute
- biologically-plausible machine-learning
The workshop will be held on December 16th, 2023 as part of the NeurIPS conference in New Orleans, Louisiana.
As GPU computing comes closer to a plateau in terms of efficiency and cost due to Moore' s law reaching its limit, there is a growing need to explore alternative computing paradigms, such as (opto-)analog, neuromorphic, and low-power computing. This NeurIPS workshop aims to unite researchers from machine learning and alternative computation fields to establish a new hardware-ML feedback loop. By co-designing models with specialized accelerators, we can leverage the benefits of increased throughput or lower per-flop power consumption. Novel devices hold the potential to further accelerate standard deep learning or even enable efficient inference and training of hitherto compute-constrained model classes. However, new compute paradigms typically present challenges such as intrinsic noise, restricted sets of compute operations, or limited bit-depth, and thus require model-hardware co-design. This workshop's goal is to foster cross-disciplinary collaboration to capitalize on the opportunities offered by emerging AI accelerators.