Results for “embedded-linux”

Deploy MATLAB-generated code to embedded hardware using Embedded Coder. Use when configuring code generation for microcontrollers (STM32, Raspberry Pi, ARM Cortex), setting up PIL/SIL verification, disabling dynamic memory allocation, or configuring hardware-specific code generation settings. Covers ERT-based configurations, processor-in-the-loop testing, memory constraints, and the MEX→SIL→PIL verification progression.

Deploy and operate the VSS 3.2 GA RT-Embed Video Embedding microservice using Docker Compose, covering GPU prerequisites, REST API usage for file uploads, text/video embeddings, live RTSP streams, Redis/Kafka/OTel integration, and troubleshooting.

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

Extract Jetson Linux BSP and sample-rootfs tarballs, run apply_binaries.sh with the correct GPU stack flag, and record the image metadata in the active target profile.

Scaffold a one-command dev-local launcher for any codebase by investigating services, ports, and infra dependencies, then generating a single script with tmux session management.

Build and deploy applications on the inference.sh platform using Python or Node.js, with CLI scaffolding, local testing, and cloud deployment.

Configure Fluentd and Fluent Bit for centralized log aggregation, routing, filtering, and enrichment across distributed infrastructure.

Builds AWS Nitro Enclave-based confidential computing environments with cryptographic attestation, KMS policy integration, and secure vsock communication for processing sensitive data.

Deploy kernel-level runtime security monitoring on Linux hosts or Kubernetes clusters using eBPF and Cilium Tetragon for process execution tracking, network observability, file access auditing, and runtime enforcement.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Host-based simulation using the Zephyr native_sim board. Covers building for Linux/macOS/Windows, automated testing, host-side debugging (GDB, Valgrind), and host-target integration. Trigger when developing application logic without hardware or setting up CI/CD tests.

Runs 150+ AI apps in the cloud via the inference.sh CLI, covering image generation, video creation, LLMs, web search, 3D generation, and Twitter automation.

Generate, verify, refine, and accelerate C/C++ or CUDA code from MATLAB with MATLAB Coder, Embedded Coder, GPU Coder, or MATLAB Test. Also covers writing codegen-ready MATLAB code: language constraints, coder.* directives, and optimization patterns. Triggers on: codegen, MEX, deploy MATLAB as C/C++, GPU Coder, coder.screener, coder.config, coder.gpuConfig, coder.typeof, coder.runTest, matlabtest.coder.TestCase, SIL, embedded config, no dynamic memory, EnableMexProfiling, coder.timeit, coder.perfCompare, %#codegen, writing codegen-ready MATLAB, code generation readiness, coder.varsize, coder.unroll, coder.noImplicitExpansionInFunction, coder.ceval, coder.inline, coder.extrinsic, coder.const, coder.classSignature, class codegen limitations, temporal types codegen, DMA-off, stack-only, host-target InstructionSetExtensions, SIMDAcceleration, OptimizeReductions, host SIMD tuning, host OpenMP, codegen performance.

Grow firmware on any hardware through HTTP: upload C source, compile on the device, and apply with watchdog rollback.

Run Unreal Engine build and automation test workflows for Tactics through MoonMind's shared Docker backend, with a portable direct-Docker fallback outside MoonMind.

Measure Jetson DRAM and NvMap usage, capture before/after baselines, and verify memory reclamation with live audit data.

Plan and apply safe, reversible headless-mode changes on Jetson devices to reclaim memory from the GUI and non-essential daemons.