Welcome to the MLNCP Workshop at NeurIPS 2024!
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 14th/15th, 2024 as part of the NeurIPS conference in Vancouver, Canada.
Abstract
Digital computing is approaching fundamental limits and faces serious challenges in terms of scalability, performance, and sustainability. At the same time, generative AI is fuelling an explosion in compute demand. There is, thus, a growing need to explore non-traditional computing paradigms, such as (opto-)analog, neuromorphic hardware, and physical systems. Expanding on last year's successful NeurIPS workshop, which was the first of its kind in this community, we aim to bring together researchers from machine learning and alternative computation fields to establish new synergies between ML models and non-traditional hardware. Co-designing models with specialized hardware, a feature that has also been key to the synergy of digital chips like GPUs and deep learning, has the potential to offer a step change in the efficiency and sustainability of machine learning at scale. Beyond speeding up standard deep learning, new hardware may open the door for efficient inference and training of model classes that have been limited by compute resources, such as energy-based models and deep equilibrium models. So far, however, these hardware technologies have fallen short due to inherent noise, device mismatch, a limited set of compute operations, and reduced bit-depth. As a community, we need to develop new models and algorithms that can embrace and, in fact, exploit these characteristics. This workshop aims to encourage cross-disciplinary collaboration to exploit the opportunities offered by emerging AI accelerators both at training and at inference.
