Storage Device Bandwidth Optimization via Neural Network Prediction
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Solution Overview
Problem
Current data storage devices in autonomous vehicles lack efficient bandwidth allocation mechanisms for different types of operations, which can lead to performance bottlenecks and suboptimal storage media access, particularly in environments with varying workload demands from advanced driver assistance systems (ADAS) and other applications.
Innovation Solution
Implementing an artificial neural network (ANN) within the data storage device to predict and optimize bandwidth allocation schemes based on real-time data access patterns, operating parameters, and performance indicators, using unsupervised or supervised learning techniques to determine the most efficient allocation for read/write operations and background processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bandwidth is allocated uniformly to all operations, then device complexity is reduced, but productivity decreases due to performance bottlenecks
Solution Approach 1:
The patent implements dynamic bandwidth allocation where the storage device controller continuously monitors operation types (read, write, background) and adjusts bandwidth distribution in real-time based on current workload demands, transitioning from static uniform allocation to adaptive dynamic allocation
Solution Approach 2:
The system changes the bandwidth parameter dynamically by allocating different bandwidth portions to different operation types based on priority and current system state, allowing the bandwidth parameter to vary over time rather than remaining fixed
2Productivity
If bandwidth is allocated to prioritize certain operations, then productivity is improved, but loss of time occurs due to context switching and allocation overhead
Solution Approach 1:
The controller performs preliminary classification of incoming operations into categories (read, write, background) and pre-allocates appropriate bandwidth portions to each category before actual data transfer begins, avoiding last-minute allocation decisions that would cause delays
Solution Approach 2:
The system implements feedback mechanisms where the controller monitors operation completion status and system workload, continuously adjusting bandwidth allocation to maintain optimal performance while minimizing response time variations
3Productivity
If bandwidth allocation is optimized for specific operation types, then productivity increases, but adaptability decreases when workload patterns change
Solution Approach 1:
The bandwidth allocation system is designed to be dynamic and adaptive, continuously monitoring workload patterns and adjusting allocation ratios based on current demands rather than being fixed for specific operation types
Solution Approach 2:
The bandwidth allocation mechanism serves multiple functions by handling different operation types (read, write, background) with a single unified controller that can dynamically adjust allocation, making the system versatile across varying workload patterns
Data Source
AI summary
Systems, methods and apparatus of intelligent bandwidth allocation to different types of operations to access storage media in a data storage device. For example, a data storage device of a vehicle includes: storage media components; a controller configured to store data into and retrieve data from the storage media components according to commands received in the data storage device; and an artificial neural network configured to receive, as input and as a function of time, operating parameters indicative a data access pattern, and generate, based on the input, a prediction to determine an optimized bandwidth allocation scheme for controlling access by different types of operations in the data storage device to the storage media components. The controller is configured to schedule the operations of the different types to access the one or more storage media components according to the optimized bandwidth allocation scheme.