Tensor Memory Address Routing for Mixed-Dimension Computation
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Solution Overview
Problem
Existing memory address computation systems are inefficient in handling tensors with varying dimensions, particularly when they do not match common dimensions like powers of two, leading to suboptimal performance and computation speed.
Innovation Solution
Adaptive memory address computation systems that utilize a first and a fast address logic pipeline, along with configuration registers and control logic circuits, to selectively route outputs based on tensor dimensions, optimizing computation for common dimensions such as powers of two.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a single address logic pipeline is used to handle all tensor dimensions, then the system maintains simplicity and universality, but computation speed deteriorates for tensors with common dimensions like powers of two
Solution Approach 1:
The address logic pipeline is segmented into multiple specialized pipelines: a first address logic pipeline for general-purpose handling and a fast address logic pipeline optimized for common dimensions (powers of two). This segmentation allows the system to route specific tensor dimension types to the fastest appropriate pipeline, improving overall computation speed while maintaining manageable complexity through modular design.
Solution Approach 2:
The system dynamically selects which address logic pipeline to use based on the tensor dimensions being processed. Control logic circuits evaluate the tensor dimension characteristics and dynamically route the computation to either the first address logic pipeline or the fast address logic pipeline, enabling the system to adapt its complexity level to the specific computational task at hand.
2Productivity
If the address logic pipeline is optimized for common dimensions, then computation speed improves for those cases, but performance deteriorates for tensors with varying or non-common dimensions
Solution Approach 1:
The first address logic pipeline serves as a universal handler capable of processing tensors with any dimension, including varying and non-common dimensions. Meanwhile, the fast address logic pipeline provides specialized optimization for common dimensions. The control logic coordinates these two pipelines to ensure that both universal handling and specialized optimization are available, maintaining high productivity for common cases while preserving adaptability for all cases.
Solution Approach 2:
Control logic circuits act as intermediaries between the tensor dimension parameters and the address logic pipelines. These control circuits evaluate the tensor dimensions and mediate the selection process, routing appropriate tensor types to the fast pipeline while directing other cases to the first pipeline, thereby balancing productivity and adaptability.
3Speed
If multiple address logic pipelines are implemented, then computation speed improves for specific cases, but system complexity increases
Solution Approach 1:
The fast address logic pipeline is specifically optimized with local quality for handling common dimensions (powers of two), while the first address logic pipeline maintains general-purpose functionality. This localized optimization allows the system to achieve high speed for the most common cases without requiring all pipelines to be equally complex, thereby managing overall system complexity while improving performance where it matters most.
Data Source
AI summary
Systems and methods for adaptive memory address computations based on tensor dimensions are disclosed herein. A system for generating a memory address for a tensor stored in a data tile in memory may include an instruction line with an unpack instruction identifying a location in the tensor, a first address logic pipeline outputting a first address output, a fast address logic pipeline outputting a fast address output, and a control logic circuit. The control logic circuit routes one and only one of the first address output or the fast address output to be used as the memory address to access the tensor. This system enables efficient and adaptive memory address computation based on tensor dimensions, allowing for optimized performance when processing tensors with common dimensions while maintaining flexibility for handling various tensor sizes.