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9th Workshop on Accelerated Machine Learning (AccML)

Co-located with the HiPEAC 2027 Conference
(https://www.hipeac.net/2027/glasgow/)

January 19, 2027
Glasgow, United Kingdom
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CALL FOR CONTRIBUTIONS
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Machine learning and AI continue to reshape computing, driving demand for acceleration across the full system stack. The rise of foundation models, generative AI, large language and multimodal models, retrieval-augmented and agentic systems, and capable edge and on-device deployments has expanded accelerated machine learning beyond traditional convolutional networks. Today’s workloads combine dense and sparse computation, long-context attention, recommender systems, graph processing, vision-language reasoning, speech, video, and real-time inference, challenging hardware, software, and programming abstractions. These models are compute-, memory-, communication-, and energy-intensive, making acceleration essential to reduce cost and power consumption in data centers, enable low-latency inference on edge and embedded devices, and support sustainable AI deployment. Progress requires advances in heterogeneous architectures, specialized accelerators, chiplet-based systems, emerging semiconductor and near-/in-memory technologies, low-precision arithmetic, compilers, runtimes, numerical libraries, profiling, benchmarking, and deployment tools. This forum invites contributions on emerging acceleration methods, scalable computation paradigms, hardware/software co-design, and ML/AI-driven design and optimization of efficient and sustainable computing systems.

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Links to the Workshop page
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Organizers: https://accml.dcs.gla.ac.uk/workshop-2027-hipeac.html

HiPEAC: https://www.hipeac.net/2027/glasgow/#/program/8325/

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Topics
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Topics of interest include (but are not limited to):

- Novel ML/AI systems: heterogeneous multi-/many-core systems, GPUs, NPUs/TPUs, ASICs, FPGAs, and chiplet-based accelerators;
- Software ML/AI acceleration: programming models, languages, primitives, libraries, compilers, runtimes, and frameworks;
- Novel ML/AI hardware accelerators and associated system software;
- Emerging semiconductor, near-/in-memory, analog, photonic, and neuromorphic technologies for ML/AI hardware acceleration;
- ML/AI for the design, optimization, autotuning, and management of hardware, compilers, runtimes, and systems;
- Cloud, data-center, edge, embedded, and on-device ML/AI computing: hardware and software to accelerate training and inference;
- Hardware/software co-design techniques for efficient model training and inference, including quantization, sparsity, pruning, compression, efficient attention, and distillation;
- Training, fine-tuning, serving, and deployment of foundation models, LLMs, multimodal models, large GNNs, recommender systems, agentic AI, and retrieval-augmented generation;

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Invited Speakers
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Anton Lokhmotov (KRAI): "KRAI's vision on Accelerated Accelerator Programming".
Miquel Moreto (BSC): "Designing HPC Architectures to Accelerate AI at BSC"
* Other invited speakers will be announced before the paper submission deadline.

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Submission
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Papers will be reviewed by the workshop's technical program committee according to criteria regarding the submission's quality, relevance to the workshop's topics, and, foremost, its potential to spark discussions about directions, insights, and solutions in the context of accelerating machine learning. Research papers, case studies, and position papers are all welcome.

In particular, we encourage authors to submit work-in-progress papers: To facilitate sharing of thought-provoking ideas and high-potential though preliminary research, authors are welcome to make submissions describing early-stage, in-progress, and/or exploratory work in order to elicit feedback, discover collaboration opportunities, and spark productive discussions.

The workshop does not have formal proceedings.

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Important Dates
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Submission deadline: November 13, 2026
Notification of decision: November 27, 2026

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Organizers
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José L. Abellán (University of Murcia)
Valentin Radu (University of Sheffield)
Ulysse Beaugnon (Google DeepMind)
José Cano (University of Glasgow)