Abstract: An improved search operation includes receiving, by a server computing device, an encrypted search query and cleartext metadata associated with the encrypted search query from a client computing device; performing a search using the encrypted search query to generate encrypted search results; and sending the encrypted search results to the client computing device.

Abstract: An improved AR/VR operation includes receiving, by a server computing device, encrypted AR/VR user data and cleartext metadata associated with the encrypted AR/VR user data from a client computing device; getting server data based at least in part on cleartext metadata; encoding the server data; performing an AR/VR process on the encrypted AR/VR user data and the encoded server data to generate encrypted AR/VR results; and sending the encrypted AR/VR results to the client computing device.

Abstract: A method comprises receiving, from a remote device, an audio/video input signal, identifying one or more objects in the audio/video input signal tagged as a sensitive object, evaluating a set of workload requirements for a set of processing workloads comprising portions of the audio/video input signal, selecting one or more heavy processing workloads in the set of processing workloads to send to a compute service provider, in response to a determination that the one or more heavy processing workloads comprises one or more objects tagged as a sensitive object, encrypting the one or more objects tagged as a sensitive object using a homomorphic encryption protocol to generate a first homomorphically encrypted string, and sending the first homomorphically encrypted string to the compute service provider via a privacy protected communication channel.

Abstract: An apparatus to facilitate optimization of a convolutional neural network (CNN) is disclosed. The apparatus includes optimization logic to receive a CNN model having a list of instructions and including pruning logic to optimize the list of instructions by eliminating branches in the list of instructions that comprise a weight value of 0.

Abstract: A method comprises receiving, from an input device, an input speech signal, encoding the input speech signal to generate a first homomorphically encrypted string, sending the homomorphically encrypted string to a remote device via communication link, receiving, from the remote device, a reply comprising a second homomorphically encrypted string, decoding the second homomorphically encrypted string into an output speech signal, and outputting the output speech signal on an audio output device.

Abstract: An apparatus to facilitate workload scheduling is disclosed. The apparatus includes one or more clients, one or more processing units to processes workloads received from the one or more clients, including hardware resources and scheduling logic to schedule direct access of the hardware resources to the one or more clients to process the workloads.

Abstract: A method comprises receiving, from a remote device, a first encrypted data set encrypted using a first encryption scheme, performing a set of computations on the first encrypted data set to generate a first set of encrypted results, encrypting the first set of encrypted results using a second encryption scheme to generate a second set of encrypted results, sending the second set of encrypted results to the remote device, receiving, from the remote device, third set of encrypted results in which the first encryption scheme has been decrypted, and generating a set of decrypted results by applying a decryption algorithm to the third set of encrypted results to decrypt the second encryption scheme.

Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.

Abstract: One embodiment provides a parallel processor comprising a hardware scheduler to schedule pipeline commands for compute operations to one or more of multiple types of compute units, a plurality of processing resources including a first sparse compute unit configured for input at a first level of sparsity and hybrid memory circuitry including a memory controller, a memory interface, and a second sparse compute unit configured for input at a second level of sparsity that is greater than the first level of sparsity.

Abstract: An apparatus to facilitate workload scheduling is disclosed. The apparatus includes one or more clients, one or more processing units to processes workloads received from the one or more clients, including hardware resources and scheduling logic to schedule direct access of the hardware resources to the one or more clients to process the workloads.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.

Abstract: Described herein is a graphics processor comprising a memory device and a graphics processing cluster coupled with the memory device. The graphics processing cluster includes a plurality of graphics multiprocessors interconnected via a data interconnect. A graphics multiprocessor includes circuitry configured to load a modular neural network including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject.

Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.

Abstract: A mechanism is described for facilitating fast data operations and for facilitating a finite state machine for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting input data to be used in computational tasks by a computation component of a processor including a graphics processor. The method may further include determining one or more frequently-used data values (FDVs) from the data, and pushing the one or more frequent data values to bypass the computational tasks.

Abstract: One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising a decode unit to decode a single instruction into a decoded instruction, the decoded instruction to cause the compute apparatus to perform a complex compute operation.

Abstract: Embodiments provide systems and methods which facilitate optimization of a convolutional neural network (CNN). One embodiment provides for a non-transitory machine-readable medium storing instructions that cause one or more processors to perform operations comprising processing a trained convolutional neural network (CNN) to generate a processed CNN, the trained CNN having weights in a floating-point format. Processing the trained CNN includes quantizing the weights in the floating-point format to generate weights in an integer format. Quantizing the weights includes generating a quantization table to enable non-uniform quantization of the weights and quantizing the weights from the floating-point format to the integer format using the quantization table. The operations additionally comprise performing an inference operation utilizing the processed CNN with the integer format weights.

Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.

Abstract: An apparatus to facilitate workload scheduling is disclosed. The apparatus includes one or more clients, one or more processing units to processes workloads received from the one or more clients, including hardware resources and scheduling logic to schedule direct access of the hardware resources to the one or more clients to process the workloads.

Abstract: A mechanism is described for facilitating fast data operations and for facilitating a finite state machine for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting input data to be used in computational tasks by a computation component of a processor including a graphics processor. The method may further include determining one or more frequently-used data values (FDVs) from the data, and pushing the one or more frequent data values to bypass the computational tasks.

Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.