Unanswered questions

Hello Axelera AI Team,I’m Nathan Davies, Founder and Chairman of KOPO, a U.S.-based technology company developing an integrated digital ecosystem for the U.S. and African markets.We are currently evaluating edge-AI hardware and acceleration technologies for Nathan, KOPO’s AI platform, across several classes of devices and infrastructure.Our interest in Axelera AI is broader than a single development board. We would like to understand whether your technology could support a scalable edge-AI architecture across applications such as smart devices, merchant/payment hardware, intelligent terminals, mobility, vision-enabled systems and other KOPO hardware platforms.In particular, we are interested in understanding:Which current Axelera products/platforms would be most appropriate for embedded and edge inference deployments. Supported AI frameworks, models and development toolchains. Performance and power characteristics for on-device inference. Availability of SDKs/APIs and developer hardwar

I have filed a few issues on Github. The first one is a week old and hasn’t been looked at. I’ve uncovered a few plumbing issues today as well. Most of what Wingman ships is already readable: the VS Code base and the bundled Codex engine are open source, and the wingman-dashboard extension ships as unminified JavaScript. Publishing the source here would cost little and would help in a few concrete ways:Contributions instead of reports. Several of the open issues are one-line fixes. With the source here, they could arrive as pull requests. Packaging for more distributions. Packagers for Arch, Fedora and others could build Wingman properly instead of repackaging the .deb. Trust. For a tool that runs an agent with shell access on developers' machines, being able to review the code is a strong selling point, especially for a European company competing with much larger vendors.If publishing the whole codebase isn't an option, opening just the Linux launcher (/usr/bin/wingman) and the packag

My Axelera Embedded 111c (Metis Compute Board) cannot communicate with the Metis AIPU. I need help solving the issue .Board details- BSP: 1.3.3, flashed over USB with upgrade_tool (kernel 6.1.148-rockchip-standard)- Driver: patched to 1.6.2 by installing kernel-module-metis-6.1.148-rockchip-standard_v1.8.0-r0_arm64.deb and axelera-container_1.0-r0_arm64.deb (as described in the SDK 1.8.0 release notes). /sys/class/metis/version shows 1.6.2.- Voyager SDK: 1.8.0 (axversion reports 1.8.0, axversion --driver reports 1.6.2)- Did axdevice ever work on this board before? noSymptoms- axdevice reports: "Device communication timed out", "Failed to get valid board type for device metis-0:1:0 got 12", and board_type=unknown (not responding).- axdevice --refresh -v reports: "USR_DMA_XFER failed: Connection timed out", "Failed to write ELF to device memory", and "Failed to load runtime stage0".- lspci shows the device: 01:00.0 Processing accelerators [1200]: Axelera AI Metis AIPU (rev 02) [1f9d:1100

Hi,I’m planning to install a Metis M.2 accelerator inside a Lenovo ThinkPad T480.The T480’s main M.2 storage connection is PCIe Gen3 x2, and I’m considering either the Embedded 110m or the 113m 8 GB.I mainly need to confirm:Do both the 110m and 113m work correctly on PCIe Gen3 x2? Can they be power-limited to around 7–8 W if necessary? If yes, which one would you recommend for a laptop setup focused on ROS2, robotics, computer vision, and some VLM/LLM inference? Is a custom heatsink + small fan enough for this kind of installation?The laptop runs Ubuntu on x86-64.Thanks!

Hi,I am running voyager-sdk v1.8 and trying to compile a single GeMM model for 4 AIPU cores so that all 4 cores cooperate on a single inference. According to the CompilerConfig reference, MulticoreMode lists cooperative as a valid value, described as cores cooperating on a single inference.When I set multicore_mode to cooperative with aipu_cores_used set to 4 and resources_used set to 1.0, the compilation fails. I will attach the full traceback.Using batch mode instead compiles successfully, however the input shape gets promoted from batch 1 to batch 4.My questions are: what is the intended use of cooperative mode and how does it differ from batch mode in practice? Is it available in v1.8 or is it something still in development? And if cooperative is not the right tool here, is there a recommended way to have all 4 cores work together on a single inference? Attached the python script i am using for compiling. I am using axrunmodel for running the compiled ”model”. Output of axdevice:De

 I am using the Axelera Embedded 110m module (Metis AIPU) on a NanoPC-T6 Plus board.Please note that I am using the NanoPC-T6 Plus (LPDDR5 architecture), not the standard NanoPC-T6 (LPDDR4X). The official image provided for the standard NanoPC-T6 does not resolve the PCIe memory allocation failure (code:error -12) on the T6 Plus due to hardware and Device Tree differences between the two boards.I have tested the official Ubuntu 22.04 and Ubuntu 24.04 OS images provided on the official NanoPC-T6 Plus wiki page, as well as the Armbian 26.8.3 NanoPC LTS image, but the issue persists on all of them.Here are the troubleshooting steps I have attempted so far:Added the recommended kernel parameters (scode]pci=realloc=on[/code] and ncode]pcie_aspm=off[/code]) to the boot configuration. Decompiled and manually updated the hcode]ranges[/code] property in the Device Tree (ecode].dtb[/code]) to expand the non-prefetchable MMIO window for the PCIe controller node. Performed a full cold power cycle