An introduction to a remarkable case of implementing computer vision on an ESP32-S3 microcontroller, which costs just $2, by utilizing SIMD technology.
Imagine a toy with a tiny $2 chip—less than the price of a cup of coffee—that recognizes your face and checks who has arrived at your door. Until now, Artificial Intelligence (AI) was the exclusive domain of computers equipped with expensive graphics cards (GPUs, high-performance components specialized for image processing) or massive cloud servers. However, that landscape is about to change.
Recently, the developer community has been buzzing with news that ‘Computer Vision’—a technology where devices analyze and understand visual data—has been successfully implemented on a ‘microcontroller’ (a tiny computer chip for controlling home appliances or small devices) costing just $2 [Source 1, Source 14].
Why is this important?
This news is exciting because of ‘cost’ and ‘accessibility.’ When people think of AI, they often imagine massive data centers and enormous power consumption. But if this technology can run on a $2 chip, the story changes entirely. It means that immediate decision-making capabilities can be granted to smart home devices, wearable gadgets, or even very inexpensive sensor equipment. In other words, it means countless objects around us can transform into intelligent devices that ‘see’ and make decisions on their own, without needing separate, expensive equipment.
Easy Understanding: What is SIMD?
How could this tiny chip process heavy image data? The key lies in an acceleration method called ‘SIMD (Single Instruction, Multiple Data)’ [Source 1, Source 7].
To put it simply, imagine you hand out math problems to hundreds of students and have them solve them individually. Checking each student’s work one by one would be very slow. But what if you grouped them into teams of 10 and gave the same computation command simultaneously? You could solve the problems much faster. This ‘simultaneous processing power’ is the core of SIMD. It is a technology that processes multiple data points at once with a single command.
The star of this case, the ‘ESP32-S3’ microcontroller, is equipped with an upgraded ‘Cadence Xtensa LX7’ processor core. Thanks to this core, it can efficiently utilize advanced data processing techniques like SIMD, ultimately allowing heavy tasks like computer vision to run on such a lightweight chip [Source 12].
Current Situation: How much is possible?
Currently, this technology is in an early stage, proving that basic visual judgment is possible even on low-end hardware [Source 16]. Of course, a $2 chip cannot outperform the computing power of a high-performance server. However, it is highly valuable in that it can implement AI features that help with small conveniences in our daily lives, such as simple motion detection or categorizing objects, at a very low cost.
What will happen in the future?
There will be far more home appliances equipped with ‘intelligence’ in the future. We will see autonomous devices that can grasp situations on their own using just a single chip, rather than smart devices that become useless if the internet connection is lost. If optimization techniques that deliver excellent efficiency at a low cost continue to evolve, AI will permeate our lives much deeper and more quietly than it does now.
MindTickleBytes’ AI Reporter Perspective
Massive and grandiose artificial intelligence models are not the only answer. Rather, this case, which overcomes hardware limitations with software wisdom, is a great example of how technology creates meaningful changes in real life.
References
-
Vue HN2.0 SIMD-accelerated computer vision on… (https://vue-hackernews-ssr-5cavbdjcta-ew.a.run.app/item/50236681) - SIMD-Accelerated Computer Vision on the ESP32-S3 (https://360techadvisors.com/simd-accelerated-computer-vision-on-the-esp32-s3-one-of-the-fun-parts-of-the-esp32-s3-microcontroller-is-that-it-got-upgraded-to-the-newer-cadence-xtensa-lx7-processor-core-which-turns-out-to-have-a)
- Today in HN - 2024-06-25 • Buttondown (https://buttondown.com/MorningHackers/archive/today-in-hn-2024-06-25/)
- HackerNewsDaily Top 10 @2024-06-26 · Issue #1447 (https://github.com/headllines/hackernews-daily/issues/1447)
- Article URL: https://shraiwi.github.io/read.html?md… (https://vk.ru/wall-55993443_54916)
- GPU
- SIMD
- FPGA
- ESP32-S3
- Raspberry Pi
- Arduino Uno
- Because AI models are getting bigger
- Because it enables simple AI features without expensive equipment
- Because it uses more electricity