Neural Network Model Caching in SRAM to Avoid Reload Latency
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Solution Overview
Problem
Conventional hardware accelerators face significant latency and power consumption due to the need to reload and recompile neural network models frequently, as they can only store parameters for a single model at a time, leading to inefficient memory usage and processing delays.
Innovation Solution
A compiler identifies frequently invoked neural network models and compiles them in a sequence, storing the compiled models in SRAM with unique hashes, allowing for simultaneous storage and quick retrieval, thereby avoiding redundant reloading and improving processing speed and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parameters of a single neural network model are stored in the memory of the hardware accelerator, then the model can be executed efficiently, but when another model needs to be executed, parameters must be reloaded causing latency and power consumption
Solution Approach 1:
The system performs preliminary actions by identifying frequently invoked neural network models and pre-loading their parameters into the hardware accelerator memory in advance. This allows the models to be ready for execution without waiting for parameter loading at runtime, thereby reducing latency and improving execution efficiency.
Solution Approach 2:
The system dynamically adapts the memory allocation and model selection based on runtime conditions. It monitors which models are frequently invoked and adjusts which models are loaded into memory accordingly. This dynamic approach allows the system to optimize for the most commonly used models while maintaining flexibility to handle different execution scenarios.
2Speed
If parameters of frequently invoked neural network models are pre-loaded into SRAM, then processing speed improves, but memory resources are consumed
Solution Approach 1:
The system applies partial action by selectively loading only the parameters of frequently invoked neural network models into the SRAM, rather than loading all possible models. This partial loading approach optimizes the use of limited memory resources while still achieving significant performance improvements for the most commonly executed models.
Solution Approach 2:
The system changes the parameter of model frequency invocation to determine which models deserve pre-loading. By monitoring and analyzing the invocation frequency parameter, the system dynamically selects which models to load into memory, thereby optimizing the balance between processing speed and memory consumption based on actual usage patterns.
3Reliability
If the hardware accelerator memory is cleared and reloaded with compiled models, then outdated models are removed, but unnecessary clearing and reloading causes latency
Solution Approach 1:
The system implements feedback mechanisms to track which models are currently in use and which have been replaced. This feedback information is used to intelligently determine when memory clearing is actually necessary, preventing unnecessary clearing operations that would cause latency while ensuring that outdated models are properly removed when needed.
Solution Approach 2:
The system performs self-service by autonomously managing the model parameter lifecycle in memory. It automatically identifies when models need to be loaded, when they can be evicted, and when memory clearing is necessary, without requiring external intervention. This self-managed approach reduces unnecessary clearing operations and optimizes memory utilization patterns.
Data Source
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AI summary
A compiler of a computing device is described that identifies a sequence of neural network models frequently invoked by an application of the computing device, compiles the models in that sequence, and loads a static random access memory (SRAM) of a hardware accelerator with the compiled models only when the same compiled models-from another, but same, sequence that was previously invoked-are not already present in the SRAM. This prevents unnecessary reloading of compiled models into the SRAM, thereby increasing runtime speed and conserving computational energy.