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 expose embedded cast operations in at least one of a load instruction or a store instruction; determine a target precision level for the cast operations; and load the cast operations at the target precision level. Other embodiments are also disclosed and claimed.

Abstract: A mechanism is described for facilitating memory handling and data management in machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting multiple tables associated with multiple neural networks at multiple autonomous machines, where each of the multiple tables include an index. The method may further include combining the multiple tables and multiple indexes associated with the multiple tables into a single table and a single index, respectively, where the single table is communicated to the multiple autonomous machines to allow simultaneous processing of one or more portions of the single table using one or more memory devices and one or more processors of one or more of the multiple autonomous machines.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer. Other embodiments are also disclosed and claimed.

Abstract: A mechanism is described for facilitating memory handling and data management in machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting multiple tables associated with multiple neural networks at multiple autonomous machines, where each of the multiple tables include an index. The method may further include combining the multiple tables and multiple indexes associated with the multiple tables into a single table and a single index, respectively, where the single table is communicated to the multiple autonomous machines to allow simultaneous processing of one or more portions of the single table using one or more memory devices and one or more processors of one or more of the multiple autonomous machines.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer.

Abstract: An apparatus and method are described for detecting and correcting data fetch errors within a processor core. For example, one embodiment of an instruction processing apparatus for detecting and recovering from data fetch errors comprises: at least one processor core having a plurality of instruction processing stages including a data fetch stage and a retirement stage; and error processing logic in communication with the processing stages to perform the operations of: detecting an error associated with data in response to a data fetch operation performed by the data fetch stage; and responsively performing one or more operations to ensure that the error does not corrupt an architectural state of the processor core within the retirement stage.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: A method of providing infrastructure protection for a network that includes IP addresses as low as a single IP address. An end user sends traffic to an IP address of a projected server publicly available as an anycast address, and sends traffic to the protected network. The traffic is routed via one of several scrubbing centers using the public IP address as anycast address, and the scrubbing center provides infrastructure protection by scanning and filtering the incoming traffic for illegitimate data. After filtering, the legitimate traffic is encapsulated, e.g., via including virtual GRE tunnel information that includes a secret IP address known only to the scrubbing center and the protected server that receives the network traffic. The protected server decapsulates the network packet and responds back to the end user via the scrubbing network.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: A method of providing infrastructure protection for a server of a network organization, the method including announcing, as an internet protocol (IP) address associated with a server of a plurality of servers, a first anycast IP address, the first anycast IP address being one of a plurality of anycast IP addresses that each serve as an anycast address for a scrubbing center network. Each of the plurality of anycast IP addresses is allocated to a respective server of the plurality of servers by the scrubbing center network. The scrubbing center network may receive an incoming network packet intended for the server, the incoming network packet identified using the first anycast IP address. The scrubbing center network may determine whether the incoming network packet is legitimate and if so, the incoming network packet may be routed to the server using a generic routing encapsulation (GRE) tunnel.

Abstract: A method of providing infrastructure protection for a server of a network organization, the method including announcing, as an internet protocol (IP) address associated with a server of a plurality of servers, a first anycast IP address, the first anycast IP address being one of a plurality of anycast IP addresses that each serve as an anycast address for a network of edge servers. Each of the plurality of anycast IP addresses is allocated to a respective server of the plurality of servers by the network of edge servers. The network of edge servers may receive an incoming network packet intended for the server, the incoming network packet identified using the first anycast IP address. The network of edge servers may determine whether the incoming network packet is legitimate and if so, the incoming network packet may be routed to the server using a generic routing encapsulation (GRE) tunnel.

Abstract: A method of providing infrastructure protection for a server of a network organization, the method including announcing an IP address range associated with the network organization using a border gateway protocol on an edge router of a scrubbing center associated with the network organization. The method further including receiving an incoming network packet intended for a server of the network organization identified using a public IP address within the IP address range, the public IP address serving as a first anycast address for a plurality of scrubbing centers in a distributed network of scrubbing servers, the plurality of scrubbing centers including the scrubbing center. The method further including determining, by the scrubbing center, whether the incoming network packet is legitimate. The method further including, responsive to determining that the incoming network packet is legitimate, routing, by a processor, the incoming network packet to the server at a private IP address.

Abstract: A method of providing infrastructure protection for a server of a network organization, the method including announcing an internet protocol (IP) address range associated with the network organization using a border gateway protocol (BGP) on an edge server of a distributed network of edge servers. The method further including receiving an incoming network packet intended for the server of the network organization identified using a public IP address within the IP address range, the public IP address serving as a first anycast address for a distributed network of edge servers. The method further including determining, by the distributed network, whether the incoming network packet is legitimate. The method further including responsive to determining that the incoming network packet is legitimate, routing, by a processor using generic routing encapsulation (GRE), the incoming network packet to the server at a private IP address.

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 expose embedded cast operations in at least one of a load instruction or a store instruction; determine a target precision level for the cast operations; and load the cast operations at the target precision level. Other embodiments are also disclosed and claimed.

Abstract: A mechanism is described for facilitating memory handling and data management in machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting multiple tables associated with multiple neural networks at multiple autonomous machines, where each of the multiple tables include an index. The method may further include combining the multiple tables and multiple indexes associated with the multiple tables into a single table and a single index, respectively, where the single table is communicated to the multiple autonomous machines to allow simultaneous processing of one or more portions of the single table using one or more memory devices and one or more processors of one or more of the multiple autonomous machines.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer. Other embodiments are also disclosed and claimed.

Abstract: An apparatus and method are described for detecting and correcting data fetch errors within a processor core. For example, one embodiment of an instruction processing apparatus for detecting and recovering from data fetch errors comprises: at least one processor core having a plurality of instruction processing stages including a data fetch stage and a retirement stage; and error processing logic in communication with the processing stages to perform the operations of: detecting an error associated with data in response to a data fetch operation performed by the data fetch stage; and responsively performing one or more operations to ensure that the error does not corrupt an architectural state of the processor core within the retirement stage.

Abstract: A processor includes a decode unit to decode an instruction that is to indicate a first source packed data operand that is to include at least four data elements, to indicate a second source packed data operand that is to include at least four data elements, and to indicate one or more destination storage locations. The execution unit, in response to the instruction, is to store at least one result mask operand in the destination storage location(s). The at least one result mask operand is to include a different mask element for each corresponding data element in one of the first and second source packed data operands in a same relative position. Each mask element is to indicate whether the corresponding data element in said one of the source packed data operands equals any of the data elements in the other of the source packed data operands.

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 expose embedded cast operations in at least one of a load instruction or a store instruction; determine a target precision level for the cast operations; and load the cast operations at the target precision level. Other embodiments are also disclosed and claimed.