What question did this study set out to answer?

The main aim is to enhance memory access efficiency in AI tensor processing using a new data mapping technique.

March 19, 2026Open Access

Efficient Multidimensional Data Mapping for Low-Power AI Tensor Processing

Key Points

The main aim is to enhance memory access efficiency in AI tensor processing using a new data mapping technique.
Application of a patented data mapping technique originally designed for semiconductor memory.
Analysis of memory access patterns in AI workloads.
Development of a conceptual framework for integrating the mapping method into tensor operations.
Reduces unnecessary memory activations, leading to lower power consumption.
Enhances computational efficiency during tensor processing.

Abstract

Efficient management of multidimensional data is critical for high-performance AI tensor and image processing. This paper explores the application of a patented data mapping technique (US7907473, JP5133073), originally designed for semiconductor memory, to improve memory access locality in AI workloads. By efficiently mapping multidimensional tensors according to their coordinates, the approach reduces unnecessary memory activations, potentially lowering power consumption and enhancing computational efficiency. We present a conceptual framework for integrating this method into AI tensor operations, analyze its potential benefits in terms of memory access patterns, and discuss implications for low-power AI system design.

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