The scale and complexity of modern artificial intelligence workloads, particularly the rise of agentic AI and multi-modal foundational models, have outpaced the capabilities of monolithic compute nodes. As the industry scales, the future of AI infrastructure relies on the effective disaggregation of workloads across heterogeneous hardware.
The CHASM workshop aims to bring together researchers and practitioners from computer architecture, systems software, and machine learning to address the critical bottlenecks in disaggregated AI. We seek to explore the full stack of challenges from hardware-level interconnects and memory pooling to the systems software required to compile, orchestrate, and verify highly dynamic execution graphs across heterogeneous accelerators.
Systems Software and Compilation
Compilation frameworks and intermediate representations for disaggregated, heterogeneous environments
Compilation, scheduling, and runtime orchestration of agentic AI workloads as dynamic execution graphs
Formal verification, debugging, and equivalence checking for distributed tensor algebra and parallel execution
Online adaptation, test-time training, and adaptive inference compute in deployed agentic AI systems on disaggregated infrastructure
Architecture and Hardware
Architectures for scalable memory disaggregation and pooling in AI clusters
High-performance interconnects and networking protocols tailored for heterogeneous accelerator communication
Hardware-software co-design for composable AI systems
Characterization and Evaluation
Characterization of agentic, multi-modal, and tool-augmented Ai workloads on heterogeneous systems
Marginal cost-efficiency analysis and performance benchmarking of contemporary AI accelerators in distributed topologies
Novel simulation and evaluation methodologies for disaggregated datacenter architectures
Telemetry, profiling, and bottleneck analysis in large-scale heterogeneous AI deployments
Coming Soon
We solicit both full papers (8-10 pages) and short/position papers (4-6 pages). Submissions are double-blinded. The page limit includes figures, tables, and appendices, but excludes references. Please use standard LaTeX or Word ACM templates. All submissions will need to be made via EasyChair (link) . Each submission will be reviewed by at least three reviewers from the program committee. Papers will be reviewed for novelty, quality, technical strength, and relevance to the workshop. All accepted papers will be published here.
Submission Deadline: 8/28/26
Acceptance Notification: 9/11/26
Camera-Ready Submission Deadline: 9/25/26
Workshop Date: 11/1/26
Coming Soon
Tom St. John (Gimlet Labs) - tomstjohn617@gmail.com
Ankita Nayak (Gimlet Labs) ankitanayak@gimletlabs.ai
Christina Giannoula (MPI-SWS) - christina.giann@gmail.com
Qijing "Jenny" Huang (NVIDIA) - jennyhuang@nvidia.com
Coming Soon