Neural Network Code Compilation for Specialized Processors
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Solution Overview
Problem
Existing solutions struggle to deploy machine learning models on specialized processors with limited computing power and memory resources, often requiring additional processing by more powerful CPUs or GPUs, which consumes extra power and resources.
Innovation Solution
The technology compiles neural network models into code that optimizes memory usage by reusing memory portions and allocating all memory at compile time, based on the resource constraints of the target device, thereby avoiding dynamic memory allocation and deallocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If neural network models are deployed on specialized processors with limited resources, then power consumption and resource usage are reduced, but the ability to execute complex machine learning operations is worsened
Solution Approach 1:
The patent applies preliminary action by performing memory allocation and optimization during the code generation phase rather than at runtime. The system analyzes the neural network model structure in advance, determines optimal memory allocation strategies, and generates specialized code that embeds these pre-computed memory configurations. This allows the specialized processor to execute operations efficiently without needing complex runtime memory management, thus reducing power consumption while maintaining execution capability.
Solution Approach 2:
The patent changes parameters by transforming the neural network model representation into a format optimized for specialized processors. This includes converting standard neural network operations into equivalent operations that utilize the specialized processor's specific instruction set and memory architecture. The code generation process adjusts operational parameters such as memory access patterns, data layout, and computation sequences to match the constraints and capabilities of the target hardware, enabling effective execution on resource-constrained devices.
2Adaptability or versatility
If memory is allocated dynamically during runtime, then flexibility in memory usage is improved, but processing overhead and power consumption increase
Solution Approach 1:
The patent eliminates runtime memory allocation overhead by performing all memory allocation decisions during code generation. The system analyzes the neural network model's memory requirements in advance and generates code with static memory allocations that are optimized for the specific model structure. This preliminary planning removes the need for complex runtime memory management operations, significantly reducing processing overhead and power consumption while maintaining sufficient flexibility through the optimized static allocation strategy.
Solution Approach 2:
The patent extracts the memory management functionality from the runtime execution phase and relocates it to the code generation phase. By separating memory allocation concerns from execution, the system generates self-contained code that has all memory allocations predetermined and embedded. This extraction eliminates the runtime overhead of dynamic memory management while preserving the essential flexibility needed for neural network operations through the pre-computed allocation strategy.
3Power
If additional powerful processors are used for processing, then computational capability is improved, but resource consumption and complexity increase
Solution Approach 1:
The patent enables the specialized processor to serve itself by generating code that is fully optimized for its specific architecture. The code generation system creates self-contained executables that utilize the specialized processor's native instruction set, memory structure, and operational characteristics without requiring assistance from external powerful processors. This self-service capability allows the specialized processor to independently execute neural network operations, eliminating the need for additional CPU or GPU resources and reducing overall system complexity.
Solution Approach 2:
The patent enhances the universality of the specialized processor by creating a code generation system that can adapt neural network models to the processor's specific capabilities. The generated code translates various neural network operations into the specialized processor's instruction set, enabling it to handle diverse machine learning tasks without requiring additional specialized hardware. This multi-functionality approach allows a single specialized processor to effectively perform multiple neural network operations that would otherwise require multiple different processor types.
Data Source
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AI summary
The subject technology receives a neural network model in a model format, the model format including information for a set of layers of the neural network model, each layer of the set of layers including a set of respective operations. The subject technology generates neural network (NN) code from the neural network model, the NN code being in a programming language distinct from the model format, and the NN code comprising a respective memory allocation for each respective layer of the set of layers of the neural network model, where the generating comprises determining the respective memory allocation for each respective layer based at least in part on a resource constraint of a target device. The subject technology compiles the NN code into a binary format. The subject technology generates a package for deploying the compiled NN code on the target device.