Machine Learning Node Training With Fixed Function Logical Representation
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Solution Overview
Problem
Existing solutions for computer vision tasks in embedded devices face challenges due to resource constraints, as machine learning processes struggle to replicate the operations of fixed function nodes, which are fixed in silicon, leading to inefficiencies in power, space, and latency.
Innovation Solution
The implementation of an end-to-end training method for a machine learning node that interfaces with a logical representation of a fixed function node, allowing the machine learning node to parameterize its usage through software emulation, thereby optimizing resource consumption and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning processes are used to solve computer vision tasks in embedded devices, then adaptability and versatility are improved, but resource consumption (power, space, latency) increases
Solution Approach 1:
The system segments the computer vision processing into two distinct parts: a fixed function node that handles resource-intensive, deterministic operations, and a machine learning node that handles adaptive, learning-based operations. This segmentation allows each component to operate in its optimal efficiency zone, reducing overall power consumption while maintaining adaptability.
Solution Approach 2:
A logical representation layer is introduced as an intermediary between the fixed function node and the machine learning node. This logical representation enables the machine learning node to interface with and control the fixed function node's operations, allowing efficient coordination and resource management between the two heterogeneous components.
2Power
If fixed function nodes are used in embedded devices, then power consumption and latency are reduced, but adaptability and versatility are limited
Solution Approach 1:
The fixed function node is designed with multi-functionality, capable of performing multiple computer vision operations through a unified interface. The logical representation layer enables the same hardware infrastructure to support various processing tasks, enhancing versatility without sacrificing power efficiency.
Solution Approach 2:
The logical representation acts as an intermediary that translates machine learning node requirements into fixed function node operations. This intermediary layer enables the fixed function node to adapt to different machine learning models and tasks while maintaining its power-efficient, deterministic execution characteristics.
3Productivity
If machine learning nodes interface directly with fixed function nodes, then operational efficiency is improved, but training complexity increases due to the fixed nature of silicon operations
Solution Approach 1:
A logical representation (software emulation) of the fixed function node is created as a copy that can be used during training. This logical copy replicates the fixed function node's behavior and interface, allowing the machine learning node to be trained on actual hardware characteristics without the complexity of direct hardware training, thereby reducing training complexity while maintaining operational efficiency.
4Adaptability or versatility
If software emulation of fixed function nodes is implemented, then machine learning node parameterization is enabled, but computational overhead during training increases
Solution Approach 1:
The software emulation creates a virtual model of the fixed function node that can be efficiently executed during training. This copy allows for rapid iteration and parameter optimization without the constraints of actual hardware reconfiguration, enabling comprehensive parameterization while managing training time through efficient software implementation.
Data Source
AI summary
In some implementations, a method includes: obtaining a logical representation of a fixed function node; generating, by concerted operation of the logical representation of the fixed function node and a machine learning node that interfaces with the logical representation of the fixed function node, a candidate result based on a set of image data frames; determining whether error criteria are satisfied based at least in part on a comparison between the candidate result and a predetermined result for the set of image data frames; and, in response to determining that the error criteria are satisfied, modifying at least one of: a first portion of operating parameters of the machine learning node associated with operations of the machine learning node; and a second portion of operating parameters of the machine learning node associated with interfacing operations between the machine learning node and the logical representation of the fixed function node.