AccML 2024 : 6th Workshop on Accelerated Machine Learning
Call For Papers
In this 6th AccML workshop, we aim to bring together researchers working in Machine Learning and System Architecture to discuss requirements, opportunities, challenges and next steps in developing novel approaches for machine learning systems.
The remarkable performance achieved in a variety of application areas (natural language processing, computer vision, games, etc.) has led to the emergence of heterogeneous architectures to accelerate machine learning workloads. In parallel, production deployment, model complexity and diversity pushed for higher productivity systems, more powerful programming abstractions, software and system architectures, dedicated runtime systems and numerical libraries, deployment and analysis tools. Deep learning models are generally memory and computationally intensive, for both training and inference. Accelerating these operations has obvious advantages, first by reducing the energy consumption (e.g. in data centers), and secondly, making these models usable on smaller devices at the edge of the Internet. In addition, while convolutional neural networks have motivated much of this effort, numerous applications and models involve a wider variety of operations, network architectures, and data processing. These applications and models permanently challenge computer architecture, the system stack, and programming abstractions. The high level of interest in these areas calls for a dedicated forum to discuss emerging acceleration techniques and computation paradigms for machine learning algorithms, as well as the applications of machine learning to the construction of such systems.
The workshop brings together researchers and practitioners working on computing systems for machine learning, and using machine learning to build better computing systems. It also reaches out to a wider community interested in this rapidly growing area, to raise awareness of the existing efforts, to foster collaboration and the free exchange of ideas.
This builds on the success of our previous events:
Papers should be in double column IEEE format of between 4 and 8 pages including references. Papers should be uploaded as PDF and not anonymized.
Topics of interest include (but are not limited to):
Novel ML systems: heterogeneous multi/many-core systems, GPUs and FPGAs;
Software ML acceleration: languages, primitives, libraries, compilers and frameworks;
Novel ML hardware accelerators and associated software;
Emerging semiconductor technologies with applications to ML hardware acceleration;
ML for the construction and tuning of systems;
Cloud and edge ML computing: hardware and software to accelerate training and inference;
Computing systems research addressing the privacy and security of ML-dominated systems;
ML techniques for more efficient model training and inference (e.g. sparsity, pruning, etc);
Generative AI and their impact on computational resources;