FRESHNESS AND GRAVITY OF DATA OPERATORS EXECUTING IN NEAR MEMORY COMPUTE IN SCALABLE DISAGGREGATED MEMORY ARCHITECTURES

    公开(公告)号:US20240303078A1

    公开(公告)日:2024-09-12

    申请号:US18181307

    申请日:2023-03-09

    CPC classification number: G06F9/3004 G06F9/5016

    Abstract: The disclosure provides for systems and methods for improving bandwidth and latency associated with executing data requests in disaggregated memory by leveraging usage indicators (also referred to as usage value), such as “freshness” of data operators and processing “gravity” of near memory compute functions. Examples of the systems and methods disclosed herein generate data operators comprising near memory compute functions offloaded proximate to disaggregated memory nodes, assign a usage value to each data operator based on at least one of: (i) a freshness indicator for each data operators, and (ii) a gravity indicator for each near memory compute function; and allocate data operations to the data operators based on the usage value.

    OPTIMIZING FOR ENERGY EFFICIENCY VIA NEAR MEMORY COMPUTE IN SCALABLE DISAGGREGATED MEMORY ARCHITECTURES

    公开(公告)号:US20240338132A1

    公开(公告)日:2024-10-10

    申请号:US18296197

    申请日:2023-04-05

    CPC classification number: G06F3/0625 G06F3/0629 G06F3/067

    Abstract: The disclosure includes a system and methods provide for optimizing performance of disaggregated memory architectures in terms of time and energy. Examples of the systems and methods disclosed herein provide for a near memory compute proximate to a disaggregated memory that can be implemented to receive, from a compute node, one or more requests to perform computation functions on data stored at the disaggregated memory and collect telemetry data for the disaggregated memory, a near memory compute proximate to the disaggregated memory, and the compute node. The systems and methods disclosed herein can also model a plurality of configurations for executing the one or more requests based on the telemetry data, select a modeled configuration of the plurality of modeled configurations for executing the one or more requests, and assign one or more of a plurality of data operators of the near memory compute according to the selected modeled configuration.

    SYSTEMS AND METHODS FOR RECONFIGURATION CONTROL USING CAPABILITIES

    公开(公告)号:US20190319838A1

    公开(公告)日:2019-10-17

    申请号:US15955657

    申请日:2018-04-17

    Abstract: Systems and methods for system reconfiguration of a computing system that includes a plurality of memory and computing resources, may include: assigning a reconfiguration capability to a user, the reconfiguration capability granting the user a right to reconfigure at least one of memory and computing resources in the computing system; a controller of the computing system receiving a reconfiguration request from a user for a requested system reconfiguration along with that user's configuration capability; the controller of the computing system verifying that the user from which the reconfiguration request was received has the rights to make the requested system reconfiguration; and the controller of the system executing the requested system reconfiguration if the user has the rights to make the requested system reconfiguration.

    SELF-ADAPTABLE ACCELERATORS HAVING ALTERNATING PRODUCTION/OPTIMIZING MODES

    公开(公告)号:US20240362031A1

    公开(公告)日:2024-10-31

    申请号:US18308275

    申请日:2023-04-27

    CPC classification number: G06F9/44505 G06F11/3495

    Abstract: Systems and methods are provided for an accelerator system that includes a baseline (production) accelerator, optimizing accelerator, and control hardware accelerator, and an operation of alternatingly switching the production/optimizing accelerators between production and optimizing. With two production/optimizing accelerators, at any given point in time, one accelerator adapts while another accelerator processes data. Once the second accelerator starts doing a better job (e.g., has adapted to data drift), the accelerators change their modes, and the trainable accelerator becomes the “optimized” one. The accelerators do this non-stop, thus maintaining redundancy, providing expected quality of service (QOS) and adapting to data/concept drift.

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