Address Interleaving for ML Memory Bandwidth
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Solution Overview
Problem
The inference phase of machine learning is computationally and data-intensive, leading to bottlenecks in data movement and memory access due to inefficient use of bandwidth, particularly as input data and model sizes grow, with current computing architectures not being scalable for effective processing.
Innovation Solution
The solution involves interleaving memory access addresses across multiple channels, using a master component to determine appropriate channels for memory access and shifting channel identifier bits to higher order bits, allowing efficient data access and bandwidth utilization in a hardware-based programmable architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data movement operations are performed sequentially (load, processing, store), then processing can be completed, but bandwidth efficiency deteriorates and bottlenecks increase
Solution Approach 1:
The memory address is segmented into multiple parts, with the lower-order bits used to select among multiple memory channels. This allows simultaneous data access across multiple channels, effectively parallelizing the data movement operations and eliminating the sequential bottleneck while maintaining high processing throughput.
Solution Approach 2:
The patent introduces a new dimension to memory access by using multiple memory channels simultaneously. Instead of sequential access along a single channel, the system accesses multiple channels in parallel, adding a spatial dimension to the data movement process and thereby improving bandwidth efficiency without sacrificing processing speed.
2Productivity
If data is loaded and stored using traditional sequential operations, then data processing can be performed, but time consumption increases due to three operations per data element
Solution Approach 1:
The patent merges multiple data movement operations into a single parallel operation by using interleaved addressing across multiple channels. Instead of performing load, process, and store sequentially, the system can simultaneously access multiple data elements across different channels, effectively combining multiple operations into one parallel step and reducing total time consumption.
Solution Approach 2:
The interleaved memory access pattern enables continuous data movement and processing without idle time between operations. By continuously accessing different memory channels in parallel, the system maintains useful action throughout the data processing pipeline, eliminating the time losses associated with sequential load-process-store cycles.
3Adaptability or versatility
If current computing architecture is used, then existing applications can run, but scalability for machine learning applications deteriorates as data sizes grow
Solution Approach 1:
The memory addressing system is segmented into multiple channels with interleaved addressing, allowing the architecture to scale with data sizes by activating more channels as needed. This segmentation provides a path for scalability without requiring complete architectural redesign, maintaining adaptability for growing ML workloads while managing complexity through modular channel expansion.
Data Source
AI summary
A system includes a memory, an interface engine, and a master. The memory is configured to store data. The inference engine is configured to receive the data and to perform one or more computation tasks of a machine learning (ML) operation associated with the data. The master is configured to interleave an address associated with memory access transaction for accessing the memory. The master is further configured to provide a content associated with the accessing to the inference engine.