Neural Network Memory Optimization for Edge Devices
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Solution Overview
Problem
Edge devices with limited processing capabilities and memory struggle to execute machine learning models optimally due to hardware constraints, leading to suboptimal performance when deploying models trained on high-end devices.
Innovation Solution
An optimization engine analyzes the machine learning model architecture, identifies device-specific computational resources, and compiles executable files to optimize computational cycles and memory use, including kernel modifications, quantization, and memory partitioning for efficient execution on target platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models trained on high-end devices are deployed on edge devices, then model accuracy and performance are preserved, but hardware constraints cause suboptimal execution and excessive memory usage
Solution Approach 1:
The patent applies parameter changes by modifying computational parameters including quantization of weight and activation values to lower precision formats, changing data types from float32 to int8 or lower, and adjusting computational precision to match edge device capabilities while maintaining acceptable model accuracy
Solution Approach 2:
The patent segments the neural network model into multiple components including partitioning layers into compute-intensive and memory-intensive groups, dividing weight matrices into blocks for optimized processing, and splitting computations to leverage both on-chip and off-chip memory hierarchies
2Ease of operation
If machine learning models are deployed on edge devices with limited memory, then local processing advantages are achieved, but memory constraints cause execution failures or severe performance degradation
Solution Approach 1:
The patent implements nested memory hierarchy optimization by organizing computations to utilize multiple levels of memory nesting: fastest on-chip memory for frequently accessed weights and activations, intermediate cache memory for layer outputs, and slower off-chip memory for model parameters, creating a nested access pattern that minimizes memory bandwidth requirements
Solution Approach 2:
The patent applies discarding and recovering by overwriting intermediate activation values in on-chip memory after they have been used for computation, recycling memory buffers for different purposes during different phases of inference, and reusing memory allocations across multiple inference batches to reduce peak memory requirements
3Ease of manufacture
If standard neural network execution is used on edge devices, then implementation simplicity is maintained, but computational overhead and memory access patterns are suboptimal
Solution Approach 1:
The patent applies dynamics by implementing dynamic memory allocation strategies that adapt buffer sizes and memory assignments based on the specific layer being executed, dynamically switching between different computation patterns (GEMM, convolution, depthwise separation) based on layer characteristics, and adjusting precision requirements based on layer sensitivity
Data Source
AI summary
Implementations disclosed describe methods and systems to perform the methods of deploying and executing machine learning models on target-specific computational platforms. Optimization techniques include but are not limited to alignment of kernel operations with hardware instructions of a target processing device, reduction of kernel dimensions near boundaries of data, efficient reuse of a small number of memory components during neural network operations, run-time quantization of data and neural network parameters, and other methods.