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Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury

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Efficient and accurate neural-field reconstruction using resistive memory.

Yifei Yu^1,2,3, Xinyuan Zhang^1,2,3, Shaocong Wang^1,2,3

¹Department of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.

|June 10, 2026

Summary

This summary is machine-generated.

This study introduces a new framework for reconstructing signals from incomplete data, improving efficiency and parallelism for AI applications like medical imaging and virtual reality.

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Area of Science:

Computer Science
Electrical Engineering
Artificial Intelligence

Background:

Complex signal reconstruction from sparse data is crucial for AI, medical imaging, and AR/VR.
Traditional hardware faces limitations in sampling, storage, data movement, and parallel efficiency.
Existing methods struggle with incomplete measurements and limited computational resources.

Purpose of the Study:

To present a software-hardware co-optimization framework for sparse-input signal reconstruction.
To address the challenges of traditional digital hardware in signal reconstruction tasks.
To enhance energy efficiency and parallelism in AI-driven signal processing.

Main Methods:

Utilized neural fields for implicit signal representation via neural networks.
Applied low-rank decomposition and structured pruning for signal compression.
Developed a resistive-memory-based computing-in-memory platform with a Gaussian encoder and MLP processing engine.
Implemented a hardware-aware quantization circuit for precise weight mapping.

Main Results:

Achieved significant projected gains in energy efficiency (23.5×, 21.0×, 32.3×) and parallelism (10.8×, 38.8×, 6.2×) across three applications.
Demonstrated comparable reconstruction quality to traditional methods.
Showcased the system's performance on a 40-nm 256 Kb resistive-memory macro.

Conclusions:

The developed framework offers substantial improvements in energy efficiency and parallelism for sparse-input signal reconstruction.
This work advances AI-driven signal reconstruction, enabling efficient medical AI and 3D vision applications.
The resistive-memory-based platform provides a viable solution for resource-constrained AI tasks.