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Hi ​Axelera  team,I’m running parallel and cascading pipelines together with inference.py—the pipelines initialize and generally work. The issue is input routing: during inference I’m unable to reliably select/bind the correct video (file/RTSP/USB) to each network. For example, launching two independent detectors plus one cascade with multiple media sources (e.g., ./inference.py modelA modelB media/a.mp4 media/b.mp4 --pipe=gst --verbose) often results in the streams attaching to the wrong model or failing to open, while one stream runs as expected.Is there a supported way to explicitly map inputs to models/pipelines when using inference.py? For instance: CLI flags to pin inputs by index/name (e.g., --input[0]=..., --input[1]=...) YAML fields that define per-network input URIs A recommended PipeManager pattern for deterministic source‑to‑network routing when combining parallel and cascading graphs   the command i wrote:./inference.py parellel5_yv11pose_scpmyv8_bcfyv8_yv11_ppeyv8 med

I am trying to get the Axelera Metis M.2 module working on a NanoPC-T6 Plus board powered by the Rockchip RK3588 SoC running Ubuntu 22.04 (Linux kernel 5.10.0-1012-rockchip / ARM64).(I installed image file in this link https://support.axelera.ai/hc/en-us/articles/31859388491794-Bring-up-Voyager-SDK-in-NanoPC-T6?_gl=1*3zteth*_gcl_au*MTc5ODEwMTM1MC4xNzg3NzI0Mjgx because I have tested other OS images provided on the official NanoPC-T6 Plus wiki, but the issue persists, indicating a known PCIe memory allocation bug within the Ubuntu 22.04 kernel for this platform.)However, the driver fails to probe the device due to PCI resource assignment failure (ENOMEM / error -12).1. Device Identification & lspci StatusThe PCIe device is visible on the bus (0000:01:00.0), but the Linux kernel fails to assign memory resources (MMIO) to Region 0 and Region 2:$ lspci -vv -s 0000:01:00.0 | grep -iE 'Region|Memory|flags' Region 0: Memory at <ignored> (64-bit, non-prefetchable) [disabled] Region 2:

Dear Axelera Support,I am currently following the Voyager SDK quick start guide (YOLO inference demo) on an RK3588-based Compute Board (received out-of-the-box), using Voyager SDK version 1.5.3. However, I am unable to successfully run inference due to persistent device communication issues with the Metis AIPU.--------------------------------------------------System Overview- Platform: RK3588 (Compute Board, out-of-the-box setup)- Voyager SDK: 1.5.3 (and also tested with 1.4.0)- Connection: PCIe (Metis AIPU detected as 01:00.0 [1f9d:1100])--------------------------------------------------Initial IssueRunning the quick start inference:./inference.py yolov5s-v7-coco usb:/dev/video1Result:ERROR: No devices foundAt that moment:- lspci returned no devices, indicating a PCIe enumeration issue.--------------------------------------------------After Cold BootAfter performing a full power cycle:- lspci correctly shows:  01:00.0 Processing accelerators: Axelera AI Metis AIPU (rev 02) [1f9d:1100]

Hi everyone! Thanks for joining in with The Prompt! You’ve made our job really hard, as there are some seriously awesome project ideas here.I’ve been working through them all day, compiling them for the judging panel, so there’s still time to sneak a last minute idea in if you want, while I’m getting on top of this mountain of cool ideas!The submissions will close any time now, but if you get there before the gate comes down, you’re still in!I’ll be in touch ASAP about the selection - watch this space!

Are there Axelera powered development boards similar to raspberry pi or like a nvidia jetson ?.Has axelera considered making one.

Hi team,We are currently deploying the Axelera Metis PCIe 4-AIPU AI Accelerator Card (AXE-BME20P4) in an HPE ProLiant DL380 Gen11 server as part of a k3s-based AI inference cluster.The card is physically seated in a PCIe Gen 3.0 x16 slot and Ubuntu Server 24.04 LTS is installed. However, we are facing a challenge with the auxiliary power connection.The Metis card requires a 2x4 (8-pin) PCIe auxiliary connector, but the HPE DL380 Gen11 uses a proprietary internal power header on the riser side. We have identified HPE part P77896-B21 (DL380 Gen11 2U 8-pin GPU Power Cable Kit) as the likely adapter cable needed.Before we proceed with the order, we wanted to check with you directly:1. Have you or any of your customers successfully deployed the Metis PCIe 4-AIPU in an HPE ProLiant DL380 Gen11?2. Can you confirm that HPE P77896-B21 is the correct cable to connect the Metis card's 2x4 auxiliary power connector to the DL380 Gen11's internal power distribution?3. Are there any other HPE DL380 G

Help me is tha work

I recently acquired a Metis M.2 module and was so excited to get started with it. To my horror, I installed it into a standard, modern x86 mini PC (with power disconnected, all by the books), and the system was bricked upon my first attempt to boot. At first, I thought maybe the M.2 slot on the system was not providing adequate power for the device, and to the benefit of the Metis, I decided to try another host system. HUGE mistake. The same thing happened with a LattePanda x86 SBC, confirmed to have enough power to the M-key M.2 slot. These systems are not cheap, so to say I am disappointed is an incredible understatement.  Maybe I’m just not understanding where to look for host system specs, but it doesn’t seem like this issue is warned against anywhere in your docs or on the community page here. Has anyone else had this problem? I’m down two systems, so if someone could please tell me with certainty which system I CAN reliably plug this thing into, I would be very appreciative. EDIT

We want to connect multiple camera to detect object and track it. Each camera at different location. We could do that with Yolo. In Axelera, we can do it with 1 camera. When 2nd camera is started, error is thrown. Please advice 

I’ve been running through the installation of the Voyager SDK v1.7 and I seem to be getting stuck on the operators build step.axdevice returns “Device 0: metis-0:3:0 8GiB metis-std-temp m2-max flver=1.6.0 bcver=7.4 clock=800MHz mvm=100% power-limit=8.5W”However when I try to run `make operators` I get the following error:FAILED: axstreamer/CMakeFiles/axstreamer.dir/src/AxInferenceAxRuntime.cpp.o /usr/bin/c++ -DHAVE_LIBVA -DHAVE_OPENCL -Daxstreamer_EXPORTS -I/usr/include/opencv4 -I/home/ubuntu/axelera/axelera-py/lib/python3.12/site-packages/axelera/lib/pkgconfig/../../include/include -I/home/ubuntu/axelera/voyager-sdk/operators/axstreamer/include -I/home/ubuntu/axelera/axelera-py/lib/python3.12/site-packages/axelera/lib/pkgconfig/../../include -O3 -DNDEBUG -std=gnu++20 -fPIC -Wall -Werror -Wno-sign-compare -Wno-deprecated-enum-enum-conversion -Wno-deprecated-anon-enum-enum-conversion -Wno-return-type-c-linkage -Wno-pessimizing-move -Wno-maybe-uninitialized -Wno-error=class-memaccess -MD

On a x64 based system with a Metis M.2, i am trying to do a firmware update for the device but getting an error.(axelera-env) devuser@linuxdev:~/voyager-sdk$ axdevice interactive_flash_updateWARNING: AXELERA_DEVICE_DIR environment variable not set.Using AXELERA_DEVICE_DIR=/home/devuser/voyager-sdk/axelera-env/lib/python3.12/site-packages/axelera/omegaWARNING: AIPU_RUNTIME_STAGE0_OMEGA environment variable not set.Using AIPU_RUNTIME_STAGE0_OMEGA=/home/devuser/voyager-sdk/axelera-env/lib/python3.12/site-packages/axelera/omega/bin/start_axelera_runtime_stage0.bin>>> Metis Flash Update Script>>> =========================>>> Auto-detecting and updating all connected devices...>>> Detected 1 device(s) from axdevice>>> ==============================================>>> Found 1 device(s):>>> - metis-0:81:0>>> ==============================================>>> Device metis-0:81:0 needs firmware update (current: 1.2

Mild disclosure up front: there's a sale on, and this is me telling you about it. 😄I don’t normally get too commercial in here, but the memory ceiling is one of the questions that comes up quite a lot, and the Axelera Edge 130p in its 16GB configuration is a pretty simple answer to that.Two things it actually buys you, as opposed to just "more is better":Bigger models load. The small LLMs are fine on 4GB. Llama 1B and 3B, no drama. Once you go past that it’s not much slow, as just doesn't fit, and there's not really any way round that. Model switching stops being a problem. If you're juggling four or more models and swapping between them, they all need to stay resident. 4GB runs out once a couple of those models are on the larger side. This is the one I'd underline, because the multi-model and multi-stream threads in here are pretty common.I think it’s actually a strong approach, prototyping on a nice, big 16GB card so you're not fighting memory limits while the pipeline is still unde

axros2 is an open ROS2 Jazzy integration that connects the Axelera Metis AIPU to your robotics stack, so you can run real-time vision at the edge without burning your power budget (214 TOPS at roughly 5 to 9 watts).What axros2 covers today:Real time object detection on the Metis AIPU, published as standard vision_msgs, so it drops straight into any ROS2 graph. Human pose estimation and 3D wrist tracking, enough to let a robot follow your hand or take an object handed to it. A full MoveIt2 pick and place pipeline on a Niryo NED2 arm, covering detect, approach, grip, verify, and place. Helper nodes for camera input, live overlays, and annotated capture, plus ready to run launch files and full documentation. Two integration styles shown side by side, the low level AxRuntime C++ API for tight control, and the higher level AxInferenceNet pipeline for fast model swapping.Why it matters for your application: whether you are building a mobile robot, a manipulator, or an inspection system, axro

It’s nice to be back for another interesting Axelera competition!My one-minute pitch:Advanced driving safety should not be limited to expensive new cars. MetisCopilot 360° will use four USB3 cameras and a single Metis PCIe card to provide 7 practical L0–L2 driver-assistance features, including lane, collision, blind-spot, and tailgating warnings at a fraction of the cost of comparable solutions. This is a project I have wanted to build for some time, and I believe Metis and Voyager Wingman provide the right tools to make it happen. I will use my prior Axelera HW/SW experience supercharged with the capabilities of Voyager Wingman to help build, integrate, and optimize the required multi-model pipelines rapidly, turning this idea into a working in-vehicle demonstration within one month. Business case:There is a strong business-case for a capable driver-assistance copilot aimed at lower and mid-range cars. Everyone deserves access to better safety, including those who can't afford high-en

 The Binary Solar Matrix and Chromatic Wave Nullification: A Unified Framework for Positive and Negative RealitiesAuthor: Gautam Pal*Affiliation: Independent Researcher, Nadia, West Bengal, India*Corresponding Author Email: Palgour438@gmail.comORCID: 0009-0004-3456-9972AbstractThis treatise proposes a groundbreaking cosmological paradigm that fundamentally redefines the ontological relationship between luminescence, primordial darkness, and the topological architecture of the universe. By examining the fluid-dynamic analogs of solar wave mechanics, we establish that darkness is not an absolute ontological void, but rather a highly ordered continuum of chromatic nullification governed by destructive phase interference. Extrapolating this localized optical mechanic to a macro-cosmological scale, we posit the existence of a Binary Solar Matrix, comprising the observable Positive Sun and its unmanifested inverse, the Negative Sun. This requisite duality dictates a state of symmetric equili

Goal: Bridging the gap between emergency responders and monitoring sensor systems.Long-Term Aspiration: Develop an EdgeAI application using Axelera hardware that operates locally to consolidate information from multiple monitoring systems and creates a local LLM “Avatar” that the end-user can interact with in order to “query” the emergency.Brief Description for the Current Challenge: Simulate a set of sensors generating data → run monitoring algorithms → upon an emergency trigger (manual or monitoring alarm) consolidate key data → launch a session with a locally hosted llm having the summary data as context and an optimized system prompt → run preliminary evaluation tests on the usefulness of the Avatar.Note: We might want to calibrate the Avatar for operation in a voice interfacing format in addition to the chat interface to allow both office personnel but also frontline-mobile workers to interact with the Avatar (potentially en-route to the emergency). P.S. Hello everyone! Just found