Baidu USA LLC

Kunlunxin Technology (beijing) Company Limited

Baidu.com Times Technology (beijing) Co. Ltd.

Zhibiao Zhao

Yueqiang Cheng

Yong Wang

Jian Ouyang

Hefei Zhu

Xiaozhang Gong

Qingshu Chen

Jun Huan

Jiaxin Shi

Yingzhen Yang

Baoxin Zhao

Systems and methods are disclosed for migrating a virtual machine (VM) having a virtual function that maps resources of an artificial intelligence (AI) accelerator to the VM. A driver for the AI accelerator can generate a checkpoint of VM processes that make calls to the AI accelerator, and can the checkpoint can include a list and configuration of resources mapped to the AI accelerator by the virtual function. The driver can also access the code, data, and memory of the AI accelerator to generate a checkpoint of the AI accelerator status. When the VM is migrated to a new host, then either, or both, of these checkpoint frames can be used to ensure that resuming the VM on a new host having appropriate AI accelerator resources, can be successful resumed on the new host. One or both checkpoint frames can be captured based upon an event, in anticipation of the need to migrate the VM.

Method for virtual machine migration with artificial intelligence accelerator status validation in virtualization environment

Method for virtual machine migration with checkpoint authentication in virtualization environment

Embodiments of the disclosure discloses a method and system of a virtualization environment for a data processing (DP) accelerator. In one embodiment, a data processing (DP) accelerator includes a resource management unit and one or more dynamically isolated resources managed by the resource management unit. The DP accelerator includes one or more virtual functions (VFs) each associated with one of the one or more dynamically isolated resources, where a virtual machine (VM) of a host is assigned one of the one or more VFs to access the dynamically isolated resources associated with the assigned VF, and where the VM has no access to the rest of the one or more dynamically isolated resources.

Data protection with dynamic resource isolation for data processing accelerators

Presented herein are embodiments of an improved asymmetric quantization, which may generally be referred to as improved asymmetric quantization (IAQ) embodiments. IAQ embodiments combine the benefits of conventional asymmetric quantization and symmetric quantization but also provide additional computation efficiencies. Embodiments of IAQ adopt an asymmetric range of the weights of a neural network layer, so they circumvent the limitation of symmetric range of symmetric quantization. Moreover, the inference process of a neural network quantized by an IAQ embodiment is much faster than that of the neural network quantized by conventional asymmetric quantization by quantizing an offset value of each layer.

Asymmetric quantization for compression and for acceleration of inference for neural networks

Embodiments of the disclosure discloses a method and system for a virtualization environment for a data processing (DP) accelerator. In one embodiment, a data processing (DP) accelerator includes one or more statically partitioned resources and one or more virtual functions (VFs) each associated with one of the one or more statically partitioned resources. A virtual machine (VM) of a host is assigned one of the one or more VFs to access the statically partitioned resources associated with the assigned VF. The VM has no access to the rest of the one or more statically partitioned resources of the DP accelerator.

Data protection with static resource partition for data processing accelerators

Growing community of inventors

Sunnyvale, CA, United States of America

Zhibiao Zhao