RISC-V Silicon in the Jungle Could Save the Amazon

2026-06-16 · Source: Big Data & AI News - EE Times · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices, Robotics & Autonomous Systems · Depth: Advanced, medium

Summary

Researchers at the University of São Paulo, led by Professors Marcelo Zuffo and Laisa Costa, are developing the "Internet of Trees," a real-time monitoring network for the Amazon rainforest. Introduced at the RISC-V Summit Europe 2026, this system uses open-architecture RISC-V microprocessors to deploy microsensors throughout the forest canopy. The project aims to gather crucial data, as climate models predict a critical threshold of 25.4°C, beyond which environmental conditions will rapidly deteriorate. Unlike traditional remote sensing, this distributed network uses the PULGA microcontroller, built on a 65-nm process and consuming 13.8 milliwatts, with built-in neural networks for local data processing. The team is also innovating sustainable, biodegradable materials and energy harvesting techniques, including "Flea" chips that draw power from biological sources, mimicking parasites. Funded partly by a Brazilian law allocating 1% of oil profits to research, the system also offers commercial potential for carbon credit verification and illegal activity detection, ultimately aiming to create a digital twin of the Amazon.

Key takeaway

For AI Hardware Engineers and Research Scientists developing environmental monitoring solutions, this project demonstrates a viable path for deploying robust, long-term sensor networks in extreme conditions. You should consider open-source RISC-V architectures for customizability and auditability. Prioritize biodegradable materials for sustainability. Explore bio-inspired energy harvesting and edge processing to overcome power and connectivity challenges, ensuring your systems operate autonomously for decades.

Key insights

Open-source RISC-V and sustainable, bio-inspired electronics enable real-time, distributed environmental monitoring in challenging ecosystems.

Principles

• Open-source hardware ensures auditability and customization.
• Longevity requires biodegradable, non-toxic materials.
• Local processing reduces bandwidth and power needs.

Method

Deploy a multi-layered, 3D network of RISC-V-based PULGA microcontrollers with Zephyr and Swarm OS. Process data locally using neural networks, then transmit via LoRa to cloud servers.

In practice

• Monitor carbon for carbon credit markets.
• Detect illegal logging, mining, poaching.
• Provide real-time biodiversity data.

Topics

• RISC-V
• Environmental Monitoring
• Internet of Trees
• Sustainable Electronics
• Edge Computing
• Energy Harvesting

Code references

• pulp-platform/pulpino

Best for: AI Scientist, AI Hardware Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Big Data & AI News - EE Times.