Abstract: Methods, apparatus, and processor-readable storage media for implementing an automated data center robotic system using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining one or more items of data, external to an automated robotic system, from a data center environment; determining one or more automated operations to be carried out by the automated robotic system within the data center environment by processing at least a portion of the one or more items of data and one or more user-provided commands using at least one artificial intelligence-based inference engine; generating one or more instructions related to carrying out the one or more automated operations within the data center environment; and performing at least a portion of the one or more automated operations within the data center environment based at least in part on the one or more instructions.

Abstract: An information handling system includes processing nodes, a compute express link (CXL) switch, and CXL devices. A workload orchestrator receives a workload to be instantiated on a particular one of the processing nodes, determines a set of resources associated with the workload, selects a particular one of the CXL devices to be used based upon the set or resources, and launches the workload on the processing node.

Abstract: An information handling system includes a processor, first and second plug-in connector interfaces coupled to the processor, and first and second accelerator modules installed into respective first and second plug-in connector interfaces. The processor instantiates machine learning code. The information handling system instantiates a workload on the processor. The machine learning code determines a processing need of the workload, determines a first processing capability of the first accelerator module and a second processing capability of the second accelerator module, and allocates a processing resource of the first accelerator module to the workload based upon an evaluation of the processing need, the first processing capability, and the second processing capability.

Abstract: Methods, apparatus, and processor-readable storage media for automated topology-aware deep learning inference tuning are provided herein.

Abstract: An information handling system may include at least one central processing unit (CPU), and a plurality of special-purpose processing units. The information handling system may be configured to: repeatedly monitor amounts of data traffic exchanged between respective pairs of the special-purpose processing units during selected time periods; based on the monitored amounts of data traffic, determine an amount of traffic imbalance for the plurality of special-purpose processing units; and in response to the amount of traffic imbalance exceeding a threshold amount, transmit a notification indicative of the amount of traffic imbalance.

Abstract: An information handling system may include at least one central processing unit (CPU), and a plurality of special-purpose processing units. The information handling system may be configured to: repeatedly monitor amounts of data traffic exchanged between respective pairs of the special-purpose processing units during selected time periods; based on the monitored amounts of data traffic, determine an amount of traffic imbalance for the plurality of special-purpose processing units; and in response to the amount of traffic imbalance exceeding a threshold amount, transmit a notification indicative of the amount of traffic imbalance.

Abstract: Techniques are provided for topology-aware provisioning of computing resources in a distributed heterogeneous environment.

Abstract: Techniques are provided for topology-aware provisioning of computing resources in a distributed heterogeneous environment.