Adaptive quantization for neural networks

    公开(公告)号:US11803734B2

    公开(公告)日:2023-10-31

    申请号:US15849617

    申请日:2017-12-20

    CPC classification number: G06N3/063 G06N3/08

    Abstract: Methods, devices, systems, and instructions for adaptive quantization in an artificial neural network (ANN) calculate a distribution of ANN information; select a quantization function from a set of quantization functions based on the distribution; apply the quantization function to the ANN information to generate quantized ANN information; load the quantized ANN information into the ANN; and generate an output based on the quantized ANN information. Some examples recalculate the distribution of ANN information and reselect the quantization function from the set of quantization functions based on the resampled distribution if the output does not sufficiently correlate with a known correct output. In some examples, the ANN information includes a set of training data. In some examples, the ANN information includes a plurality of link weights.

    MIXED SIGNAL FEEDBACK DESIGN FOR VERIFICATION
    206.
    发明公开

    公开(公告)号:US20230342528A1

    公开(公告)日:2023-10-26

    申请号:US17990005

    申请日:2022-11-18

    CPC classification number: G06F30/3308 G06F30/3323 G06N20/00

    Abstract: Techniques for implementing a mixed signal feedback design for verification that reduce production and verification time by enabling piecemeal verification of components of a circuit design selectively, accurately, and exhaustively before a final, overall circuit design is completed are disclosed. Circuit nodes in an emulation model are selected and mixed signal feedback is provided to the nodes in response to signals detected at the nodes such that behavior of unavailable or unverified components to be located at the nodes can be simulated. Mixed signal feedback can be provided to the node to enable verification of the emulation model without having to wait for the unverified or unavailable components to be provided or verified. A request for manufacture may be generated including aspects of the emulation model to enable verification of a fabricated circuit in a similar or identical manner to those used to verify the emulation model.

    ALTERNATIVE PROTOCOL OVER PHYSICAL LAYER
    207.
    发明公开

    公开(公告)号:US20230342325A1

    公开(公告)日:2023-10-26

    申请号:US18216908

    申请日:2023-06-30

    CPC classification number: G06F13/4282 G06F13/1689 G06F2213/0026

    Abstract: A link controller includes a Peripheral Component Interconnect Express (PCIe) physical layer circuit for coupling to a communication link and providing a data path over the communication link, a first data link layer controller which operates according to a PCIe protocol, and a second data link layer controller which operates according to a non-PCIe protocol. A multiplexer-demultiplexer selectively connects both data link layer controllers to the PCIe physical layer circuit. A protocol translation circuit is coupled between the multiplexer-demultiplexer and the second data link layer controller, the protocol translation circuit receiving traffic data from the second data link layer controller in a non-PCIe format, encapsulating the non-PCIe format in a PCIe format, and passing traffic data to the multiplexer-demultiplexer circuit.

    Error reporting for non-volatile memory modules

    公开(公告)号:US11797369B2

    公开(公告)日:2023-10-24

    申请号:US17864804

    申请日:2022-07-14

    CPC classification number: G06F11/0772 G06F3/0679 G06F11/073 G06F11/141

    Abstract: A memory controller includes a memory channel controller adapted to receive memory access requests and dispatch associated commands addressable in a system memory address space to a non-volatile storage class memory (SCM) module. The non-volatile error reporting circuit identifies error conditions associated with the non-volatile SCM module and maps the error conditions from a first number of possible error conditions associated with the non-volatile SCM module to a second, smaller number of virtual error types for reporting to an error monitoring module of a host operating system, the mapping based at least on a classification that the error condition will or will not have a deleterious effect on an executable process running on the host operating system.

    Lid Carveouts for Processor Connection and Alignment

    公开(公告)号:US20230315171A1

    公开(公告)日:2023-10-05

    申请号:US17704912

    申请日:2022-03-25

    CPC classification number: G06F1/206 H05K5/0213 H05K5/0239 H05K7/20209

    Abstract: Package lids with carveouts configured for processor connection and alignment are described. Lid carveouts are configured to align and mechanically secure a cooling device to the package lid by receiving protrusions of the cooling device. Because the lid carveouts ensure precise alignment and orientation of a cooling device relative to a package lid, the lid design enables targeted cooling of discrete portions of the lid. Lid carveouts are further configured to expose one or more connectors disposed on a surface that supports package internal components. When contacted by corresponding connectors of a cooling device, the lid carveouts enable direct connections between the package and the attached cooling device. By creating a direct connection between package components and an attached cooling device, the lid carveouts enable a high-speed connection for proactive and on-demand cooling actuation.

    DETECTING PERSONAL-SPACE VIOLATIONS IN ARTIFICIAL INTELLIGENCE BASED NON-PLAYER CHARACTERS

    公开(公告)号:US20230310995A1

    公开(公告)日:2023-10-05

    申请号:US17709904

    申请日:2022-03-31

    CPC classification number: A63F13/56

    Abstract: Systems, apparatuses, and methods for detecting personal-space violations in artificial intelligence (AI) based non-player characters (NPCs) are disclosed. An AI engine creates a NPC that accompanies and/or interacts with a player controlled by a user playing a video game. During gameplay, measures of context-dependent personal space around the player and/or one or more NPCs are generated. A control circuit monitors the movements of the NPC during gameplay and determines whether the NPC is adhering to or violating the measures of context-dependent personal space. The control circuit can monitor the movements of multiple NPCs simultaneously during gameplay, keeping a separate score for each NPC. After some amount of time has elapsed, the scores of the NPCs are recorded, and then the scores are provided to a machine learning engine to retrain the AI engines controlling the NPCs.

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