The recent developments in the research area of distributed learning, edge computing, and hardware specialization are pushing the boundaries of performance, efficiency, and scalability. There is a notable shift towards communication-aware designs in split learning frameworks, which are being tailored to handle diverse channel conditions and heterogeneous computational capabilities in IoT networks. These designs not only enhance data privacy and minimize latency but also demonstrate superior performance compared to traditional approaches by adapting to varying communication conditions.

In the realm of stateful network applications, there is a growing trend of offloading these applications to SmartNICs to achieve better performance and lower costs. Innovative compiler and runtime systems are being developed to optimize the partitioning of stateful applications, resulting in significant reductions in CPU usage and improvements in network function acceleration. These systems are also capable of dynamically adapting to traffic changes, ensuring sustained performance.

The scalability challenges posed by AI workloads on large-scale multi-chiplet accelerators are being addressed through detailed communication characterization. Insights from these studies are guiding the development of more flexible interconnect solutions at the chiplet level, aiming to enhance the performance, efficiency, and scalability of next-generation AI accelerators.

Educational initiatives are also gaining momentum, focusing on hardware specialization in the chiplet era. These efforts are crucial for the HPC community to develop the necessary expertise to leverage advanced technologies like chiplets. Hands-on mentoring and the use of modern open-source hardware tools are being emphasized to cultivate skills in custom hardware development.

Lastly, energy-aware inference on edge devices is being advanced through hardware-software co-design frameworks. These frameworks dynamically configure parameters to optimize energy consumption and meet latency thresholds, demonstrating significant energy savings and improved performance compared to cloud-only computation.

Noteworthy Papers:

• A communication-aware split learning design for IoT networks demonstrates superior performance by adapting to diverse channel conditions.
• A compiler and runtime system for offloading stateful network applications to SmartNICs achieves significant CPU savings and adapts to traffic changes.
• A hardware-software co-design framework for energy-aware inference on edge devices shows substantial energy savings while meeting latency requirements.