Streamlining development platforms has become a trend in the embedded industry, as the demand for deploying larger AI and machine-learning (ML) models at the edge increases development complexity, time, and, thereby, cost. The architectural diversity of embedded edge devices means that models must be converted, quantized or otherwise optimized, compiled for the target runtime, and empirically benchmarked and validated on the target hardware. Applying this process across a large number of hardware platforms is time-consuming and a critical bottleneck for development teams. Framework incompatibility across different silicon vendors further complicates this development. To accelerate model optimization, heterogeneous hardware, and security hardening (crucial for mission-critical applications) with real-time performance constraints, silicon vendors must optimize their development workflows. Here, we will explore how some silicon vendors have evolved their development platforms to accelerate embedded development. Figure 1: Edge AI development bottlenecks (Source: Saumitra Jagdale) NXP adds an agentic AI framework Launched in 2018, NXP Semiconductor's eIQ software development platform has evolved from a code-heavy inference engine compiler to include the GUI-based eIQ toolkit , which performs graph-level profiling and then optimizes open-source TensorFlow, PyTorch, and ONNX models in a single, unified workflow. Model optimization includes quantization, operator fusion, pruning, and architectural mapping. The platform also includes extensions that are integrated with the Nvidia TAO toolkit (from which models can be imported). The eIQ portfolio also includes the eIQ Time Series Studio, a platform for time-series AI/ML development crucial for real-time decision-making at the edge. The portfolio is tailored for NXP's i.MX 8 and i.MX 9 application processors, QorIQ Layerscape processors (LS1046A, LX219; 760A), and S32 automotive processors and microcontrollers (MCUs). Figure 2: NXP's eIQ Toolkit ML development pipeline (Source: NXP Semiconductors) At CES 2026, NXP launched the eIQ AI hub to bring the eIQ toolkit, eIQ Time Series Studio, eIQ GenAI Flow (software pipeline for large language models), and agentic AI framework to a unified, cloud-based platform. The platform allows developers to run functional simulations, hardware-farm their models, and run benchmarking tests. Furthermore, the platform provides developers with the flexibility to switch development from the cloud to on-premises if required. Along with the AI hub, the company also added an agentic AI framework to the eIQ portfolio. Effectively, NXP is trying to bring similar acceleration to agentic AI at the edge as it did with other tools in the eIQ tool suite. Additionally, NXP has made it compatible with its Ara discrete neural processing units (NPUs). Because running multi-model workflows in constrained edge environments necessitates heterogeneous computing, NXP engineered the framework to distribute inference workloads intelligently across Ara NPUs, i.MX CPU cores, and integrated GPU acceleration. TI enhances its automotive focus Catering to diverse applications, the CCStudio development ecosystem from Texas Instruments Inc. (TI) consolidates toolchains. TI says that it unites disparate hardware architectures, graphical configuration tools, compilers, and edge AI deployment into one cohesive workflow. The suite provides tools for data collection, training, optimization, and deployment of models on TI's product portfolio, spanning the MSPM0, C2000, MSP430, SimpleLink, Hercules, Sitara, Jacinto, mmWave, C6000, and C7x families. TI's resource library, Model Zoo , contains over 200 pretrained and hardware-optimized neural-network models based on PyTorch, TensorFlow, and ONNX frameworks for vision, audio, time-series, and radar use cases. These model deployments are heavily based on advanced data signaling. Similar to NXP's AI hub, developers can benchmark neural networks on T