Abstract: A programmable hardware system for machine learning (ML) operations includes a core and an inference engine. The core receives commands from a host. The commands are in a first instruction set architecture (ISA) format. The core divides the commands into a first set for performance-critical operations, in the first ISA format, and a second set of performance non-critical operations, in the first ISA format. The core executes the second set to perform the performance non-critical operations of the ML operations and streams the first set to inference engine. The inference engine generates a stream of the first set of commands in a second ISA format based on the first set of commands in the first ISA format. The first set of commands in the second ISA format programs components within the inference engine to execute the ML operations to infer data.

Abstract: A method includes receiving a set of data. The set of data is divided into a plurality of data portions. The method includes transmitting the plurality of data portions to a plurality of processing tiles, wherein each data portion of the plurality of data portions is associated with a processing tile of a plurality of tiles. Each processing tile of the plurality of tiles performs at least one local operation on its respective data portion to form a local result. The method includes exchanging local results between the plurality of processing tiles. Moreover, the method includes calculating a global value based on the local results. The method further includes performing at least one local operation by each processing tile of the plurality of tiles on its respective data portion based on the global value to form a computed result.

Abstract: A programmable hardware system for machine learning (ML) includes a core and a streaming engine. The core receives a plurality of commands and a plurality of data from a host to be analyzed and inferred via machine learning. The core transmits a first subset of commands of the plurality of commands that is performance-critical operations and associated data thereof of the plurality of data for efficient processing thereof. The first subset of commands and the associated data are passed through via a function call. The streaming engine is coupled to the core and receives the first subset of commands and the associated data from the core. The streaming engine streams a second subset of commands of the first subset of commands and its associated data to an inference engine by executing a single instruction.

Abstract: A method of protecting networks may include detecting a compromised computing device associated with a security event generated by a unified security policy from a plurality of sites within a network. A context of the compromised computing device may be extracted. The context may be propagated to a controller. The method may further include fetching from an identity services engine (ISE), user identity associated with the compromised computing device, and provisioning the controller with a dynamic list and a data policy matching the dynamic list. The method may also include advertising the dynamic list and the data policy to at least one of the plurality of sites.

Abstract: A method includes receiving a first set of data. The method also includes receiving an instruction to determine a largest value within the first set of data. The first set of data is divided into a first plurality of data portions based on a hardware architecture of a first plurality of processing elements. The first plurality of data portions is mapped to the first plurality of processing elements. Each data portion of the first plurality of data portions is mapped exclusively to a processing element of the first plurality of processing elements. Each data portion of the first plurality of data portions is processed by its respective processing element to identify a largest value from each data portion of the first plurality of data portions, wherein the processing forms a first output data comprising the largest value from the each data portion of the first plurality of data portions.

Abstract: A processing unit to support inference acceleration for machine learning (ML) comprises an inline post processing unit configured to accept and maintain one or more lookup tables for performing a tanh and/or sigmoid operation/function. The inline post processing unit is further configured to accept data from a set of registers configured to maintain output from a processing block instead of streaming the data from an on-chip memory (OCM), perform the tanh and/or sigmoid operation on each element of the data from the processing block on a per-element basis via the one or more lookup tables, and stream post processing result of the per-element tanh and/or sigmoid operation back to the OCM after the tanh and/or sigmoid operation is complete.

Abstract: A system includes a first and a second group of cores in a multicore system. Each core of the first/second group is configured to process data. Each core within the first/second group is configured to enter into an idle state in response to being idle for a first/second period of time respectively. Every idle core in the first/second group is configured to transition out of the idle state and into an operational mode in response to receiving a signal having a first/second value respectively and further in response to having a pending operation to process.

Abstract: A system to support a machine learning (ML) operation comprises an array-based inference engine comprising a plurality of processing tiles each comprising at least one or more of an on-chip memory (OCM) configured to maintain data for local access by components in the processing tile and one or more processing units configured to perform one or more computation tasks on the data in the OCM by executing a set of task instructions. The system also comprises a data streaming engine configured to stream data between a memory and the OCMs and an instruction streaming engine configured to distribute said set of task instructions to the corresponding processing tiles to control their operations and to synchronize said set of task instructions to be executed by each processing tile, respectively, to wait current certain task at each processing tile to finish before starting a new one.

Abstract: A method of converting a data stored in a memory from a first format to a second format is disclosed. The method includes extending a number of bits in the data stored in a double data rate (DDR) memory by one bit to form an extended data. The method further includes determining whether the data stored in the DDR is signed or unsigned data. Moreover, responsive to determining that the data is signed, a sign value is added to the most significant bit of the extended data and the data is copied to lower order bits of the extended data. Responsive to determining that the data is unsigned, the data is copied to lower order bits of the extended data and the most significant bit is set to an unsigned value, e.g., zero. The extended data is stored in an on-chip memory (OCM) of a processing tile of a machine learning computer array.

Abstract: A processing unit to support inference acceleration for machine learning (ML) comprises an inline post processing unit configured to accept and maintain one or more lookup tables for performing a tanh and/or sigmoid operation/function. The inline post processing unit is further configured to accept data from a set of registers configured to maintain output from a processing block instead of streaming the data from an on-chip memory (OCM), perform the tanh and/or sigmoid operation on each element of the data from the processing block on a per-element basis via the one or more lookup tables, and stream post processing result of the per-element tanh and/or sigmoid operation back to the OCM after the tanh and/or sigmoid operation is complete.

Abstract: A processing unit to support inference acceleration for machine learning (ML) comprises an inline post processing unit configured to accept and maintain one or more lookup tables for performing a tanh and/or sigmoid operation/function. The inline post processing unit is further configured to accept data from a set of registers configured to maintain output from a processing block instead of streaming the data from an on-chip memory (OCM), perform the tanh and/or sigmoid operation on each element of the data from the processing block on a per-element basis via the one or more lookup tables, and stream post processing result of the per-element tanh and/or sigmoid operation back to the OCM after the tanh and/or sigmoid operation is complete.

Abstract: A system to support a machine learning (ML) operation comprises an array-based inference engine comprising a plurality of processing tiles each comprising at least one or more of an on-chip memory (OCM) configured to maintain data for local access by components in the processing tile and one or more processing units configured to perform one or more computation tasks on the data in the OCM by executing a set of task instructions. The system also comprises a data streaming engine configured to stream data between a memory and the OCMs and an instruction streaming engine configured to distribute said set of task instructions to the corresponding processing tiles to control their operations and to synchronize said set of task instructions to be executed by each processing tile, respectively, to wait current certain task at each processing tile to finish before starting a new one.

Abstract: A system includes a power profile engine, a power measurement engine, and a power throttling signal generator. The power profile engine receives a desired power profile, e.g., a first profile current average associated with a first time duration and a second profile current average associated with a second time duration. The power measurement engine measures current being drawn and generates a first running average for the measured currents for the first time duration and generates a second running average for the measured currents for the second time duration. The power throttling signal generator generates a first power throttling signal to throttle power in response to the first running average for the measured currents being greater than the first profile current average and generates a second power throttling signal to throttle power in response to the second running average for the measured currents being greater than the second profile current average.

Abstract: A new approach is proposed that contemplates systems and methods to support post reset fuse reload for latency reduction. First, values of fuses are read once and stored into one or more load registers on an electronic device, wherein the load registers are protected. Once the values of the fuse are loaded into the load registers, a valid indicator of the load registers is set indicating that the values have been successfully loaded into the load registers. When other components of the electronic device need to access these values, the other components will check the load registers first. If it is determined that the valid indicator of the load registers is set, the stored values are read from the load registers instead of from the fuses. If the valid indicator of the load registers is not set, the values are loaded again from the fuses into the load registers.

Abstract: A new approach for supporting tag-based synchronization among different tasks of a machine learning (ML) operation. When a first task tagged with a set tag indicating that one or more subsequent tasks need to be synchronized with it is received at an instruction streaming engine, the engine saves the set tag in a tag table and transmits instructions of the first task to a set of processing tiles for execution. When a second task having an instruction sync tag indicating that it needs to be synchronized with one or more prior tasks is received at the engine, the engine matches the instruction sync tag with the set tags in the tag table to identify prior tasks that the second task depends on. The engine holds instructions of the second task until these matching prior tasks have been completed and then releases the instructions to the processing tiles for execution.

Abstract: A new approach is proposed to support hardware-based protection for registers of an electronic device. Sources requesting access to the registers are categorized into a set of internal sources that can be trusted and a set of external sources that are untrusted. The registers are classified into a set of internal registers allowed to be accessed by the internal resources only, a set of read-only external registers that can be read by the external resources in addition to accessed by the internal resources, and a set of read/write external registers that can be read and written by both the internal and the external resources. Each access request by a source to the registers includes the source type, wherein access request is granted or denied based on the matching between the source bits in the access request and the register classification bits of the one or more registers to be accessed.

Abstract: A system includes a multicore chip configured to perform machine learning (ML) operations. The system also includes a power monitoring module configured to measure power consumption of the multicore chip on a main power rail of the multicore chip. The power monitoring module is further configured to assert a signal in response to the measured power consumption exceeding a first threshold. The power monitoring module is further configured to transmit the asserted signal to a power throttling module to initiate a power throttling for the multicore chip.

Abstract: A system includes a memory, an interface engine, and a master. The memory is configured to store data. The inference engine is configured to receive the data and to perform one or more computation tasks of a machine learning (ML) operation associated with the data. The master is configured to interleave an address associated with memory access transaction for accessing the memory. The master is further configured to provide a content associated with the accessing to the inference engine.

Abstract: A system includes a power profile engine, a power measurement engine, and a power throttling signal generator. The power profile engine receives a desired power profile, e.g., a first profile current average associated with a first time duration and a second profile current average associated with a second time duration. The power measurement engine measures current being drawn and generates a first running average for the measured currents for the first time duration and generates a second running average for the measured currents for the second time duration. The power throttling signal generator generates a first power throttling signal to throttle power in response to the first running average for the measured currents being greater than the first profile current average and generates a second power throttling signal to throttle power in response to the second running average for the measured currents being greater than the second profile current average.

Abstract: A new approach for supporting tag-based synchronization among different tasks of a machine learning (ML) operation. When a first task tagged with a set tag indicating that one or more subsequent tasks need to be synchronized with it is received at an instruction streaming engine, the engine saves the set tag in a tag table and transmits instructions of the first task to a set of processing tiles for execution. When a second task having an instruction sync tag indicating that it needs to be synchronized with one or more prior tasks is received at the engine, the engine matches the instruction sync tag with the set tags in the tag table to identify prior tasks that the second task depends on. The engine holds instructions of the second task until these matching prior tasks have been completed and then releases the instructions to the processing tiles for execution.