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AGPC 2025 in conjunction with ISCA 2025 : ARM-based General-Purpose Computing: Software-Hardware Co-Optimization for Performance Acceleration | |||||||||||||||
Link: https://freddygabbay.github.io/AGPC-ISCA2025/ | |||||||||||||||
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Call For Papers | |||||||||||||||
About the Workshop
The International Symposium on Computer Architecture (ISCA) Workshop on "ARM General-Purpose Computing: Software-Hardware Co-Optimization for Performance Acceleration" aims to bring together researchers, engineers, and industry leaders to explore the latest advancements and innovations in leveraging ARM architecture for high-performance general-purpose computing in data center. With the growing importance of ARM-based systems in data centers and beyond, this workshop focuses on the synergy between software and hardware to achieve significant performance gains. We invite contributions that address novel algorithms, new benchmarks regarding performance and power, microarchitectural designs, hardware accelerators, tightly-coupled architectures, and optimization techniques tailored for ARM instruction set architecture. This workshop seeks to foster a collaborative environment where participants can share insights, challenges, and solutions to drive the future of ARM-based general-purpose computing. Important Dates Paper Submission Deadline: April 30, 2025 Notification of Acceptance: May 15, 2025 Camera-Ready Submission: May 30, 2025 Workshop Date: June 21, 2025 Topics of Interest Innovative Data Engineering Algorithms Based on ARM Instruction Set and Pipeline: Exploration of efficient algorithms optimized for ARM instruction sets and pipeline characteristics. Techniques for leveraging ARM-specific features to enhance data processing and improve throughput. Workload characterization for ARM-based enterprise applications including big data and AI applications. Case studies on real-world applications and performance improvements. Single-Core and Multi-Core High-Performance Microarchitecture Design for Typical Data Center Scenarios: Design principles for Single-Core and Multi-Core performance of ARM-based data center processors targeting high performance in data center environments. Techniques to optimize instruction-level parallelism, branch prediction, and cache utilization. Evaluation of microarchitectural innovations through simulation and benchmarking. Multi-core/SoC architecture design for highly multi-threaded data center applications: Synchronization characterization of highly multi-threaded data center applications. Network-on-chip design for multi-core architectures. NUMA effect optimizations. Hardware Accelerator Design for Data Center Typical Scenarios: Development of specialized hardware accelerators tailored for ARM-based systems in data centers. Integration of accelerators with ARM processors to offload compute-intensive tasks. Case studies on accelerator deployment and performance benefits in typical data center workloads. Architecture and Application Tightly-Coupled Innovation for Typical Data Center Scenarios: Exploration of tightly-coupled architectures that integrate ARM processors with other components (e.g., memory, accelerators). Techniques for optimizing data flow and reducing latency in tightly-coupled systems. Real-world applications and performance evaluations of tightly-coupled architectures in data centers. ARM Instruction Stream Trace Analysis and Optimization: Methods for tracing and analyzing ARM instruction streams to identify performance bottlenecks. Techniques for optimizing instruction sequences and improving execution efficiency. Tools and frameworks for instruction stream analysis and optimization in ARM-based systems. |
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