Hi NVIDIA Community,
I am an independent researcher developing a Physics-Deterministic Cyber Defense Engine (PDCDE). Unlike traditional IT security, this engine protects critical infrastructure (OT) from kinetic attacks by enforcing physical constraints on raw data streams in real-time.
The Constraint: The physics of the protected assets dictates a hard decision loop of <40ms. To achieve this, I have built a “Stream-First” architecture that bypasses traditional databases entirely. Currently, the core logic is implemented in pure Python for flexibility.
The Question: I am looking to migrate this engine to Jetson Orin at the edge. My concern is the “Determinism” of Python (GIL, Garbage Collection) in such a safety-critical loop.
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Feasibility: Can a Python-based control loop on Jetson Orin reliably maintain a <40ms jitter-free window if optimized correctly?
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DeepStream Integration: Is it viable to embed deterministic defense logic directly into a DeepStream pipeline (e.g., via Python bindings), or is it strictly recommended to offload the critical decision path to C++/CUDA for this level of safety?
I am not looking for basic inference help; I am looking for architectural advice on real-time determinism for kinetic safety.
Thanks,
Munther, Cyber-Physics Project.
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- Your model needs to control a “fly-by-wire” separate hardware. ARM Cortex-A series cores won’t do what you want. ARM Cortex-M can do what you want, but is more limited than ARM Cortex-R.
- You didn’t have a question 3, but it might be possible to have faster inference (that’s not something I can answer), but jitter and determinism itself is not really part of a Jetson. When you see information about platforms like NVIDIA Drive you see descriptions such as “functional safety”. This is half of the Cortex-R, and you don’t have to have a shadow core with Cortex-R, but it is possible; the Cortex-R itself, even without shadow cores, is designed from the start to be capable of scheduling for hard real time. Cortex-M does not have functional safety, and it can be deterministic, but it takes more effort for any given load to do so compared to Cortex-R.
Also, it is far more likely to achieve deterministic behavior with C/C++ than it is with Python. Still, it might normally be less than perfect, and thus I’ll suggest you need something “too fast” to feed something predictable and deterministic.
Hi,
Jetson supports the RT kernel.
So ideally, you can set your program to the top priority to ensure it can also get the CPU resources in time.
However, you will still need to handle the issue of Python Garbage collection and GIL.
Thanks.
Just for context, the RT kernel is “soft” realtime. Soft realtime “tries” to be deterministic, but it won’t ever achieve it on a Jetson (cache is a big example of why).
Re: Python GIL & Soft Real-time constraints
Thank you @AastaLLL and @linuxdev for the sharp insights. You hit the nail on the head regarding the Python challenges.
Here is how Cyber-Physics V2.2 addresses them structurally:
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Handling GIL: We bypass the GIL entirely by using multiprocessing (Process-based parallelism) rather than threading. The Logic Core runs on an isolated CPU affinity, separate from the Ingestion acting merely as a bridge.
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Taming the GC (Garbage Collection): We implement a ‘Zero-Allocation Hot Path’. All critical buffers (Numpy arrays/Tensors) are pre-allocated at startup. Inside the detection loop, we reuse memory rather than creating new objects. This prevents the GC from triggering strictly during the <40ms detection window.
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Soft vs. Hard RT: We agree with @linuxdev. We are not aiming for Avionics-grade Hard RT (microseconds). Our target is Industrial RT (<40ms) to close a valve or trip a breaker. In our benchmarks, Jetson’s RT Kernel + Pre-allocated Python logic consistently hits 18-25ms latency, which is well within our safety margin for kinetic defense.
We are treating Python as the ‘Orchestrator’ of C-optimized libraries, not just a scripting layer.
Would love to share a demo log if permitted!