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Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Related Experiment Video

Updated: Aug 31, 2025

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

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A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications.

Gourav Datta¹, Souvik Kundu², Zihan Yin²

¹Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA. gdatta@usc.edu.

Scientific Reports

|August 23, 2022

Summary

This summary is machine-generated.

A new Processing-in-Pixel-in-memory (P²M) approach enhances on-device AI by integrating computation within image sensors. This reduces data transfer, saving energy and improving processing speed for AI applications.

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Area of Science:

Computer Engineering
Artificial Intelligence
Image Processing

Background:

High-resolution cameras generate vast data, demanding energy-efficient on-device AI.
Current near-sensor and in-sensor processing methods face data transfer bottlenecks.
Analog-to-digital converters (ADCs) are crucial but energy-intensive steps in visual data processing.

Purpose of the Study:

To introduce a novel Processing-in-Pixel-in-memory (P²M) paradigm for energy-efficient on-device AI.
To overcome data transfer, bandwidth, and security limitations in current visual data processing pipelines.
To embed AI computations directly within CMOS image sensor platforms.

Main Methods:

Developed a Processing-in-Pixel-in-memory (P²M) paradigm customizing pixel arrays for analog computation.
Integrated support for multi-channel, multi-bit convolution, batch normalization, and ReLU directly in the sensor.
Employed a holistic algorithm-circuit co-design approach for seamless integration into CMOS image sensors.

Main Results:

P²M significantly reduces data transfer bandwidth from sensors and analog-to-digital conversions.
Achieved up to a [Formula: see text] reduction in energy-delay product (EDP) for MobileNetV2 on TinyML visual wake words.
Maintained test accuracy comparable to standard near-sensor and in-sensor processing implementations.

Conclusions:

The P²M paradigm offers a drop-in solution for embedding CNN layers within image sensors.
P²M dramatically enhances energy efficiency and reduces data bottlenecks in on-device AI visual processing.
This approach paves the way for more powerful and efficient edge AI applications.