AI Accelerator Data Movement With Address Translation Prefetch
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Solution Overview
Problem
Deep neural networks (DNNs) face significant computation costs due to high computing demands and frequent address translations in virtualized memory systems, which hinder efficient data movement and degrade performance in machine learning and artificial intelligence applications.
Innovation Solution
Implementing a data movement engine with an address translation prefetch (ATP) circuit that proactively triggers and caches address translations before data movement requests, supporting both software and hardware prefetch modes to reduce latency and improve memory access efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If address translation is performed for every memory access in virtualized memory systems, then memory management and isolation are maintained, but data movement efficiency deteriorates due to translation latency
Solution Approach 1:
The system performs address translation in advance before actual data movement is needed. The ATP circuit proactively translates virtual addresses to physical addresses and stores the translations in a translation cache, so that when data movement requests occur, the translations are already available, eliminating translation latency from the critical data movement path.
2Productivity
If address translation prefetching is implemented, then data movement independence from translation latency is achieved, but device complexity increases due to additional circuitry
Solution Approach 1:
The ATP circuit acts as an intermediary component between the data movement engine and the memory system. It receives virtual addresses from the data movement engine, performs translation proactively, and provides both the translated data and the corresponding physical addresses to the memory interface, thereby mediating the complexity away from the critical data movement path.
3Device complexity
If traditional memory access methods are used, then system simplicity is maintained, but energy efficiency deteriorates due to frequent address translations
Solution Approach 1:
The ATP circuit continuously performs address translations in the background during idle periods or when data is not being actively moved. This keeps the translation cache populated with frequently used address mappings, so that subsequent data movement operations can proceed without energy-consuming translations, maintaining continuous useful action while improving energy efficiency.
Data Source
AI summary
Efficient data movement in neural network accelerators operating within virtualized memory systems is challenged by high address translation latency and unique data access patterns. To address this challenge, address translation prefetch (ATP) mechanisms can be implemented to proactively translate virtual memory addresses before data movement. ATP can be performed in advance of any data movement or concurrently with data movement while being throttled by page transition in the data movement request stream. The ATP mechanism can enforce quotas on outstanding ATP requests, independently for read and write streams, to preserve resources for other processes running on the neural network accelerator. In dealing with competing ATP requests, the mechanism can employ weighted arbitration to balance between different types of ATP requests, utilizing a programmable ratio. The ATP mechanisms enable scalable, high-throughput neural network inference in virtualized environments, addressing data movement bottlenecks in neural network accelerator deployments.