Embedded Machine Learning brings data-driven intelligence directly into embedded devices, enabling systems to learn from data and make intelligent decisions locally. Unlike cloud-based ML, embedded ML operates within strict constraints of power, memory, and compute while still delivering real-time performance. As industries move toward smarter, autonomous, and connected products, embedded ML has become a critical enabler across automotive, industrial, consumer, and IoT applications. Avecas provides comprehensive Embedded ML services that help organizations design, implement, and validate efficient machine learning solutions for embedded platforms.
Our embedded ML solutions focus on accuracy, efficiency, reliability, and scalability.
We design embedded ML system architectures aligned with application requirements, data characteristics, and hardware constraints. Our engineers define optimal partitioning between ML models, embedded software, and hardware acceleration resources. This structured approach ensures efficient execution and long-term scalability.
Well-designed architectures enable reliable and maintainable embedded ML systems.
Embedded platforms require ML models to be lightweight and efficient. Avecas optimizes ML models using techniques such as quantization, pruning, feature selection, and model compression. We balance model accuracy with resource usage to meet real-time and power constraints.
Optimized models enable practical ML deployment on embedded devices
We develop embedded software that integrates ML inference engines with sensors, peripherals, and system logic. Our engineers ensure seamless interaction between ML components and real-time embedded software. Solutions are designed for deterministic behavior and system stability.
This ensures consistent and predictable ML-driven operation.
Avecas supports embedded ML deployment on SoCs, MCUs, DSPs, NPUs, and FPGA-based platforms. We map ML workloads to available hardware resources to achieve optimal performance and power efficiency. Our engineers optimize memory access and data pipelines.
This enables scalable and efficient embedded ML deployment.
We provide comprehensive validation and optimization support for embedded ML systems. Our engineers validate model accuracy, performance, and robustness under real-world operating conditions. We optimize systems based on validation results to ensure production readiness.
This ensures dependable and long-lasting embedded ML solutions.
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Your Partner for Intelligent Embedded Learning Systems
We support embedded ML engineering activities using industry-standard tools and proven methodologies to deliver efficient, accurate, and scalable machine learning solutions on embedded platforms.
Tools used to train, optimize, compress, and tune ML models for efficient execution on resource-constrained embedded devices.
Lightweight ML frameworks and runtime environments integrated with embedded systems for reliable on-device inference.
Deployment techniques that leverage MCUs, SoCs, DSPs, NPUs, and FPGA accelerators to improve ML performance and efficiency.
Validation and optimization processes designed to ensure accuracy, robustness, and consistent performance in real-world embedded environments.
designing advanced SoCs.
with specialized process needs.
demanding low-power solutions.
requiring safety-critical libraries.
