Embedded Edge AI & Computer Vision Consultant in Germany
I am Dr. Farshid Pirahansiah: an independent consultant for embedded edge AI and computer vision, based in Berlin, Germany and working with engineering teams in Germany and across Europe — remotely the whole working week, and on site for the milestones that need a whiteboard. 12+ years, 21 publications, 3 patents, and a working rule: the model only counts once it runs on the device in front of the customer.
The teams I work with are not looking for a general machine-learning lecture. They already have a model, a board, a deadline, and something that is too slow, too hot, or not reproducible on the hardware they have to ship. Deutschsprachige Zusammenfassung: Edge AI & Computer Vision Beratung in Deutschland.
What I do for engineering teams in Germany
- Production-ready vision pipelines — camera capture, calibration, pre/post-processing and inference wired into a system that keeps its frame rate under load.
- Hardware-level optimization — quantization, kernel- and graph-level tuning, and a realistic measurement of what the target device can actually do.
- Prototype rescue & technical due diligence — a working demo that has to become a product, or a hardware/AI decision that has to be made before the budget is committed.
- Team enablement — paired engineering and review, so the knowledge stays in your team instead of in a slide deck.
Hardware and software I optimize on
| Layer | What I work with |
|---|---|
| Edge accelerators | NVIDIA Jetson (AGX Orin, Orin Nano, Xavier), Google Coral TPU, Hailo-8, Intel NCS2, Kria KV260 FPGA |
| GPU & runtime | CUDA, TensorRT, cuDNN, OpenVINO, ONNX Runtime, GGUF/llama.cpp, MLX/CoreML on Apple silicon |
| Precision & compression | INT8 / FP16 quantization, calibration, layer fusion, structured pruning, KV-cache and memory budgeting |
| Vision & C++ | C++17/20, OpenCV, G-API, PyTorch → TensorRT deployment, Python/Numba/Cython hotspots |
| Microcontrollers & RISC-V | ESP32 / ESP32-C3 (ESP-IDF, Arduino, BLE HID, OLED UI, TinyML), RISC-V RV32/RV64 targets (ESP32-C3/C6 cores, K210/K230-class vision SoCs) |
| Systems | real-time pipelines, multi-camera synchronization, RDMA/Thunderbolt capture, thermal and power limits, MIPI/GStreamer |
Pain points I am usually called for
These are the situations that bring teams to me. Each one is a real engineering problem I have written up on this site — the links go to the notes, so you can judge the depth before we ever talk.
"Our detector runs at 4 FPS on the Jetson we already bought"
An FP32 PyTorch model on an embedded device is the classic case. The work: build a TensorRT engine for the exact target, calibrate INT8 on your own images, move pre-processing onto the GPU so the CPU stops being the bottleneck, and batch what can be batched. See CUDA development on Windows and How PyCUDA compiles and runs C kernels for the toolchain side, and the CV, DL & ML optimization guide for the method-by-method approach.
"It works on the workstation and falls apart on the device"
Memory ceilings, thermal throttling and a different operator set are three different problems that look alike. The work: measure the real budget on the device, profile where the frames are actually lost, and shrink the model to fit the memory you have. The local-LLM-on-a-weak-machine write-up is the same method applied to a language model on an 8 GB machine.
"We have cameras, but the system does not see reliably"
Calibration, sync and geometry before networks: multi-camera rigs where the frames do not line up, optical flow that breaks under fast motion or changing light, 3D reconstruction that drifts. See 3D vision & real-time multi-camera systems, Optical flow: challenges and solutions and the cabling notes for multi-camera capture.
"The problem starts below Linux: a microcontroller and a few hundred kilobytes"
ESP32 / ESP32-C3 firmware (which is a RISC-V core): sensor and camera work, BLE HID, an OLED UI, and a model small enough to fit — flashed and measured with ESP-IDF or Arduino. Working example on this site: four firmwares on one €3 RISC-V board. Broader ISA and toolchain notes: RISC-V for AI.
"We need a model on the device, without a cloud and without a GPU"
Small-model work for edge devices: choosing an architecture that fits, quantizing it, and running it locally — from a 1-bit 4B language model on an old laptop (notes) to image generation on 8 GB, plus the ESP32-C3 board work where the whole budget is a few hundred kilobytes.
How an engagement usually works
- A call about the actual bottleneck — the model, the hardware, the deadline and the accuracy that is non-negotiable. No slide deck, no discovery phase invoice.
- A short technical audit — profiling on your target device, then a written finding: what is slow, what is fixable, what will not fit, and where the risk sits.
- Implementation together — I work inside your repository and review flow, or hand your engineers a working reference they can extend. C++, Python and CUDA, in your toolchain and your CI.
- Hand-over — the pipelines, the profiling script, and the reasoning, documented so the next engineer (or the next model) can reproduce it.
You can see what day-to-day work with me looks like in Mentoring, consulting & coaching and how I work with engineers and teams.
Where I work
- Based in Berlin, Germany — remote across the whole country, same time zone, in English or German.
- On site for kick-offs, integration weeks and lab work in Berlin and anywhere in Germany reachable by train or a short flight.
- Fully remote engagements are possible anywhere the time zone works.
Credentials
- 21 publications — journal articles, conference papers and book chapters: see the list.
- 3 patents — moving-vehicle detection, facial analysis for advertising, and GAN-based face augmentation for surveillance: patent pages.
- Google Scholar — citation profile with h-index 8 and 142 citations.
- Mentoring track record — the CV coaching roadmap I teach from, and an OpenCV 5 cheat sheet used in real sessions.
- Legal & privacy — Impressum / legal notice and privacy policy.
Start here
- Email — info@pirahansiah.com with the hardware, the model and the number that has to be met.
- Book a call — calendly.com/pirahansiah/farshid.
- Everything I have published on computer vision and edge AI — the Atlas or all pages.
Consulting and engineering work is carried out as an independent contractor; research results and publication records are listed above and verifiable through the linked sources. This page is available in German as Edge AI & Computer Vision Beratung in Deutschland.
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