Programmable Bit-Sliced VLSI for Decision Tree Edge Inference
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Solution Overview
Problem
The challenges of latency and bandwidth constraints in IoT applications necessitate the need for intelligent data processing at the edge, particularly in resource-constrained devices, where conventional cloud-based processing is inefficient and power-hungry.
Innovation Solution
A programmable and scalable bit-sliced VLSI architecture for decision tree-based machine learning inference is implemented on edge devices, utilizing a custom ASIC design with an H-structure bit-slice configuration to minimize computational and memory resources, enabling efficient data processing and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud-based processing is used for IoT data, then computational power and accuracy are improved, but latency increases and bandwidth consumption increases
Solution Approach 1:
The patent segments the centralized cloud processing function into distributed edge computing nodes. Each edge device runs local ML models that process data independently, dividing the overall processing task across multiple locations. This segmentation eliminates the single-point bottleneck of cloud processing, reducing latency while maintaining accuracy through localized decision-making capabilities.
Solution Approach 2:
The patent transitions from a single-dimension centralized processing architecture to a multi-dimensional distributed edge computing architecture. By adding the spatial dimension of distribution across multiple edge nodes, the system achieves parallel processing capabilities that reduce latency while maintaining the computational accuracy of cloud-based systems.
2Power
If cloud-based processing is used for IoT data, then computational power is improved, but bandwidth consumption increases
Solution Approach 1:
The patent extracts the ML inference capability from the centralized cloud environment and embeds it directly into edge devices. This extraction eliminates the need for continuous data transmission over the network, removing the bandwidth consumption bottleneck while preserving the computational power needed for accurate data processing locally.
Solution Approach 2:
The patent introduces edge computing devices as intermediaries between IoT sensors and the cloud. These intermediaries perform local data processing and filtering, reducing the volume of data that needs to be transmitted across the network while maintaining the computational accuracy that would otherwise require cloud-based processing.
3Loss of time
If ML models are deployed on edge devices, then latency is reduced and bandwidth is saved, but device complexity increases
Solution Approach 1:
The patent applies local quality by deploying simplified ML models specifically optimized for edge device capabilities rather than using full-featured cloud-based models. Each edge device runs tailored inference models that match its specific hardware resources, achieving low-latency processing without unnecessarily increasing device complexity beyond what each individual edge device can handle.
4Use of energy by moving object
If custom ASIC architecture is used, then power consumption is reduced and processing speed is improved, but manufacturing complexity increases
Solution Approach 1:
The patent employs dynamically reconfigurable logic that can adapt its hardware configuration based on the specific ML model being executed. This dynamic approach allows the ASIC to optimize power consumption for different workloads while using standardized manufacturing processes, reducing the manufacturing complexity burden compared to completely custom static ASIC designs.
Data Source
AI summary
Methods and systems are provided herein for a decision tree layout model. A method for imprinting a decision tree layout model onto a classification chip includes receiving a plurality of target model requirements. One or more decision tree-based inference models are loaded based on the plurality of target model requirements. Training data is obtained. The one or more decision tree-based inference models are trained using a depth layer. Predictions corresponding to the training data are generated using the one or more decision tree-based inference models. Prediction parameters associated with the plurality of predictions is determined. The prediction parameters are compared to the target model requirements. An inference model is selected from the one or more decision tree-based inference models, based on the comparison. A transistor layout is generated based on the selected inference model.