2026 Design Contest using Phytec’s phyBOARD®-RT1170 Development Kit

GridSense™ – Intelligent Energy Orchestration Platform

GridSense™ is an embedded controller designed to manage and coordinate multiple energy sources including utility power, solar PV, battery storage, generators, and controllable loads. The objective is to determine energy demand, evaluate available resources, and deploy energy where it is needed while prioritizing renewable generation whenever possible.

Using the PHYTEC phyBOARD®-RT1170, the system will communicate with meters, inverters, battery management systems, and generator controllers using industrial protocols such as Modbus and Ethernet. A custom decision engine, OptiFlow™, will continuously evaluate system conditions including load demand, battery state of charge, renewable production, and source availability to make real-time energy allocation decisions.

The prototype will demonstrate renewable-first energy management, battery optimization, load prioritization, automatic failover during outages, and a web-based dashboard showing real-time energy flows and system status.

The long-term goal is to develop a scalable platform that can be deployed in residential, commercial, and industrial applications to improve energy efficiency, resiliency, and utilization of renewable resources.

Thermal Imaging Diagnostic scanner (Wand)
A thin scanner with medical thermal cameras used to track inflammation, skin infections, or circulatory blood flow blocks without touching the patient.

The vision is to develop a diagnostic thermal imaging platform, an entire interconnected ecosystem that includes the scanner combined with advanced software, mobile apps, and data-management systems that allows to analyze, store, and report on the thermal data across multiple devices.

The scanner (called as wand) connects a digital thermal sensor grid, (the Melexis MLX90640) (a 32×24 thermal pixel array), via the I²C bus. This sensor internally converts thermal radiation to temperature values and streams the data digitally. The data matrix is small enough that the Cortex-M4 can fetch it rapidly.

The Cortex-M7 takes the raw temperature matrix, uses its 2D PXP Graphics Engine to smoothly interpolate the grid from a blocky 32×24 array into a high-resolution color heat map, and blends it on the screen over a normal camera preview from the MIPI-CSI camera port

The prototype for the thermal Imaging Diagnostic Wand will be developed using PHYTEC phyBOARD-RT1170.

RTCam - Industrial GigE Vision 2.2 Camera powered by i.MX RT1170

1. The future AI market demand
As AI infrastructure drastically increases, the demand for high-quality, uncompressed, and zero-latency machine vision system will skyrocket.
RTCam is a MCU alternative to the commercial industrial streaming cameras.
The goal is to build a fully compliant GigE Vision 2.2 industrial camera using the NXP i.MX RT1176, delivering deterministic real-time performance, instantaneous boot times, and low power consumption without the complexity and overhead of a Linux-based MPU.

2. Hardware & Acquisition
The project leverages the phyBOARD®-RT1170 Development Kit interfaced with a Sony IMX296LQR-C image sensor (Raspberry Pi Global Shutter Camera) via the MIPI-CSI interface.
The global shutter is mandatory for industrial automation to eliminate rolling-shutter artifacts on high-speed conveyor belts and robotic arms.

3. Software Architecture (Zephyr & LwIP)
The firmware will run entirely on Zephyr RTOS, utilizing the LwIP network stack optimized for the board’s Gigabit Ethernet port.
The core development focuses on two pillars of the GigE Vision standard:
GVCP (GigE Vision Control Protocol): Implementation of the UDP-based control plane. This handles device discovery and maps the GenICam XML registers, allowing the host to configure sensor parameters (Exposure, Gain, ROI) in real-time.
GVSP (GigE Vision Streaming Protocol): The performance bottleneck.
This handles the high-throughput UDP data plane, requiring strict management of IP packet fragmentation to stream uncompressed RAW/RGB video at Gigabit speeds with zero packet loss.

4. Technical Highlight: L1 Cache & eDMA Optimization
Raw CPU clock speed (1 GHz Cortex-M7) is not enough to sustain Gigabit network throughput from MIPI to the ENET_1G MAC.
To demonstrate a deep understanding of the ARM architecture, I will implement a “Zero-Copy” data pipeline using eDMA in tandem with extensive L1 D-Cache management.
MIPI RX buffers will be strictly aligned to the 32-byte cache line.
Precise Cache Invalidation (before read) and Cache Cleaning (after CPU write) routines will be implemented to ensure cache coherency during asynchronous DMA transfers.

5. Validation Strategy
Firmware robustness will be verified using industry-standard machine vision tools:
GVCP (Control Plane): Validated with Aravis (open-source framework) for device discovery, XML extraction, and register read/write stress testing.
GVSP (Data Plane): Validated with Pleora eBUS Player to certify stable, high-framerate streaming and zero packet-drop reliability.

6. What’s Next: The Path to Production
Once the firmware is validated on the development kit, the immediate next step is designing a custom, 4-layer carrier board tailored to the ultra-compact (30x30 mm) phyCORE®-RT1170 System on Module.
This board will only house the Gigabit PHY, power management (PoE), and mechanical connector for the C mount lenses.
The final result will be a production-ready, ultra-compact industrial cube camera.