Method and apparatus for efficient programmable instructions in computer systems

    公开(公告)号:US11422812B2

    公开(公告)日:2022-08-23

    申请号:US16451804

    申请日:2019-06-25

    Inventor: Andrew G. Kegel

    Abstract: Systems, apparatuses, and methods for implementing as part of a processor pipeline a reprogrammable execution unit capable of executing specialized instructions are disclosed. A processor includes one or more reprogrammable execution units which can be programmed to execute different types of customized instructions. When the processor loads a program for execution, the processor loads a bitfile associated with the program. The processor programs a reprogrammable execution unit with the bitfile so that the reprogrammable execution unit is capable of executing specialized instructions associated with the program. During execution, a dispatch unit dispatches the specialized instructions to the reprogrammable execution unit for execution. The results of other instructions, such as integer and floating point instructions, are available immediately to instructions executing on the reprogrammable execution unit since the reprogrammable execution unit shares the processor registers with the integer and floating point execution units.

    SELF-REGULATING POWER MANAGEMENT FOR A NEURAL NETWORK SYSTEM

    公开(公告)号:US20220229712A1

    公开(公告)日:2022-07-21

    申请号:US17712380

    申请日:2022-04-04

    Abstract: A neural network runs a known input data set using an error free power setting and using an error prone power setting. The differences in the outputs of the neural network using the two different power settings determine a high level error rate associated with the output of the neural network using the error prone power setting. If the high level error rate is excessive, the error prone power setting is adjusted to reduce errors by changing voltage and/or clock frequency utilized by the neural network system. If the high level error rate is within bounds, the error prone power setting can remain allowing the neural network to operate with an acceptable error tolerance and improved efficiency. The error tolerance can be specified by the neural network application.

    Protecting host memory from access by untrusted accelerators

    公开(公告)号:US11030117B2

    公开(公告)日:2021-06-08

    申请号:US15650252

    申请日:2017-07-14

    Abstract: A host processor receives an address translation request from an accelerator, which may be trusted or un-trusted. The address translation request includes a virtual address in a virtual address space that is shared by the host processor and the accelerator. The host processor encrypts a physical address in a host memory indicated by the virtual address in response to the accelerator being permitted to access the physical address. The host processor then provides the encrypted physical address to the accelerator. The accelerator provides memory access requests including the encrypted physical address to the host processor, which decrypts the physical address and selectively accesses a location in the host memory indicated by the decrypted physical address depending upon whether the accelerator is permitted to access the location indicated by the decrypted physical address.

    SELF-REGULATING POWER MANAGEMENT FOR A NEURAL NETWORK SYSTEM

    公开(公告)号:US20190235940A1

    公开(公告)日:2019-08-01

    申请号:US15884638

    申请日:2018-01-31

    CPC classification number: G06F11/076 G06K9/03 G06N3/02 G06N5/04 G06N20/00

    Abstract: A neural network runs a known input data set using an error free power setting and using an error prone power setting. The differences in the outputs of the neural network using the two different power settings determine a high level error rate associated with the output of the neural network using the error prone power setting. If the high level error rate is excessive, the error prone power setting is adjusted to reduce errors by changing voltage and/or clock frequency utilized by the neural network system. If the high level error rate is within bounds, the error prone power setting can remain allowing the neural network to operate with an acceptable error tolerance and improved efficiency. The error tolerance can be specified by the neural network application.

    METHOD AND APPARATUS FOR EFFICIENT PROGRAMMABLE INSTRUCTIONS IN COMPUTER SYSTEMS

    公开(公告)号:US20220382550A1

    公开(公告)日:2022-12-01

    申请号:US17886855

    申请日:2022-08-12

    Inventor: Andrew G. Kegel

    Abstract: Systems, apparatuses, and methods for implementing as part of a processor pipeline a reprogrammable execution unit capable of executing specialized instructions are disclosed. A processor includes one or more reprogrammable execution units which can be programmed to execute different types of customized instructions. When the processor loads a program for execution, the processor loads a bitfile associated with the program. The processor programs a reprogrammable execution unit with the bitfile so that the reprogrammable execution unit is capable of executing specialized instructions associated with the program. During execution, a dispatch unit dispatches the specialized instructions to the reprogrammable execution unit for execution. The results of other instructions, such as integer and floating point instructions, are available immediately to instructions executing on the reprogrammable execution unit since the reprogrammable execution unit shares the processor registers with the integer and floating point execution units.

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