Memory Sub-System PMIC Power Allocation Using Machine Learning
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Solution Overview
Problem
Memory sub-systems face limitations in power efficiency due to imposed power limits, hindering further performance enhancements despite advancements in data transfer rates and storage densities, necessitating a need for improved power management techniques.
Innovation Solution
Implementing a machine learning model to dynamically adjust power distribution within the memory sub-system using operational characteristics, such as power consumption, temperature, and workload, to optimize power delivery to components like memory devices and controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If power limits are imposed on memory sub-systems, then power consumption is controlled, but performance enhancement is hindered
Solution Approach 1:
The patent applies dynamics by transitioning from static power limit imposition to dynamic power distribution. The machine learning model continuously monitors operational characteristics (temperature, workload, power consumption) and adjusts power allocation in real-time, allowing the system to adapt power distribution to current operational conditions rather than enforcing fixed limits.
Solution Approach 2:
The patent changes the parameter of power distribution from a fixed constraint to a dynamically adjustable variable. By using machine learning to predict optimal power levels based on operational characteristics, the system modifies power parameters adaptively, enabling performance enhancement while maintaining power consumption within acceptable boundaries.
2Use of energy by moving object
If power distribution is statically managed, then system simplicity is maintained, but power efficiency cannot be optimized
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the host system and memory sub-system components. This intermediary analyzes operational characteristics and makes intelligent power distribution decisions, bridging the gap between simple static power management and complex dynamic optimization without requiring direct complex control logic in the memory sub-system hardware.
Solution Approach 2:
The patent replaces traditional mechanical/power-based static power management with an intelligence-based machine learning system. Instead of using fixed hardware power limits or simple software controls, the system uses machine learning algorithms to automatically optimize power distribution based on analyzed operational patterns.
3Productivity
If higher data transfer rates and storage densities are implemented, then performance is improved, but power consumption increases
Solution Approach 1:
The patent applies partial action by allocating power dynamically based on actual operational needs rather than providing maximum power continuously. The machine learning model determines the precise power level required for current workload conditions, providing only the necessary power for achieving data transfer and storage operations without excessive power consumption.
Data Source
AI summary
A current workload is received from a host system. One or more characteristics of the current workload and one or more operational characteristics of a memory sub-system is provided as input to a machine learning model. The machine learning model is trained to identify one or more parameters and corresponding predicted parameter values of a power management integrated circuit (PMIC) of the memory sub-system. The one or more parameters and corresponding predicted parameter values are used to distribute power one or more components of the memory sub-system. An output of the machine learning model is obtained. The output includes the one or more parameters and corresponding predicted parameter values. The one or more parameters of the PMIC is adjusted based on the one or more parameters and corresponding predicted parameter values.