HCW 2026 (Heterogeneity in Computing Workshop) is a ICORE C conference held in New Orleans, Louisiana, USA on 2026-05-26. The paper submission deadline is 2026-02-06. Acceptance notifications are sent on 2026-02-27.
Topics
Topics of interest include but are not limited to the following areas:
Heterogeneous multicore systems and architectures: Design, exploration, and experimental analysis of heterogeneous computing systems such as Graphics Processing Units, heterogeneous systems-on-chip, Artificial Intelligence chips, Field Programmable Gate Arrays, big.LITTLE, and application-specific architectures.
Heterogeneous parallel and distributed systems: Design and analysis of computing grids, cloud systems, hybrid clusters, datacenters, geo-distributed computing systems, and supercomputers.
Deep memory hierarchies: Design and analysis of memory hierarchies with SRAM, DRAM, Flash/SSD, and HDD technologies; NUMA architectures; cache coherence strategies; novel memory systems such as phase-change RAM, magnetic (e.g., STT) RAM, 3D Xpoint/crossbars, and memristors.
On-chip, off-chip, and heterogeneous network architectures: Network-on-chip (NoC) architectures and protocols for heterogeneous multicore applications; energy, latency, reliability, and security optimizations for NoCs; off-chip (chip-to-chip) network architectures and optimizations; heterogeneous networks (combination of NoC and off-chip) design, evaluation, and optimizations; large-scale parallel and distributed heterogeneous network design, evaluation, and optimizations.
Programming models and tools: Programming paradigms and tools for heterogeneous systems; middleware and runtime systems; performance-abstraction tradeoff; interoperability of heterogeneous software environments; workflows; dataflows.
Resource management and algorithms for heterogeneous systems: Parallel algorithms for solving problems on heterogeneous systems (e.g., multicores, hybrid clusters, grids, or clouds); strategies for scheduling and allocation on heterogeneous 2D and 3D multicore architectures; static and dynamic scheduling and resource management for large-scale and parallel heterogeneous systems.
Modeling, characterization, and optimizations: Performance models and their use in the design of parallel and distributed algorithms for heterogeneous platforms; characterizations and optimizations for improving the time to solve a problem (e.g., throughput, latency, runtime); modeling and optimizing electricity consumption (e.g., power, energy); modeling for failure management (e.g., fault tolerance, recovery, reliability); modeling for security in heterogeneous platforms.
Applications on heterogeneous systems: Case studies; confluence of Big Data systems and heterogeneous systems; data-intensive computing; scientific computing.
This year we wish to focus on and expand submissions and presentations in the following “hot topics” areas; therefore, we especially invite submissions in the following four areas:
Heterogeneous Integration of Quantum Computing: Design, exploration, and analysis of architectures and software frameworks enabling heterogeneous integration of classical computing and quantum computing (e.g., heterogeneous quantum computers, error correction, heterogeneous applications that use both classical and quantum logic, benchmarks for heterogeneous quantum computers).
Heterogeneity and Interoperability in Software & Data Systems: Design, exploration, and analysis of architectures and software frameworks for interoperability in software and data systems (e.g., semantic frameworks, interoperability for heterogeneous Internet-of-Things systems, model-driven frameworks).
Heterogeneous Computing for Machine Learning (ML) and Deep Learning (DL): Design, exploration, benchmarking, and analysis of accelerators and software frameworks for ML and DL applications on heterogeneous computing systems.
Closing the loop on the design of heterogeneous compilers, runtimes, and hardware: As the needs of heterogeneous hardware apply pressure on runtime designers to adjust for the complexities of heterogeneous resource management, runtimes are now applying pressure back towards compiler designers to include all relevant information – such as data flow and dependency analysis or hardware-specific representations of application tasks – in their binaries to enable resource management policies to arbitrate effectively. Advancements in machine understanding of code are critical in enabling progress here with a holistic view of compilers, runtimes and heterogeneous hardware.
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