Tiny Machine Learning (TinyML) Solutions Are Being Defined by Software

Author: Reece Hayden

Tiny Machine Learning (TinyML) solutions are increasingly being defined by innovative software functionality, aimed at unlocking new use cases and simplifying model deployment. This post takes a look at seven key vendors in the market, including how they leverage acquisitions/partnerships and how they are advancing their portfolios through software integrations.

Tiny Machine Learning Market Overview

Tiny Machine Learning (TinyML) chipset vendors are contending with increased competition as the hardware has rapidly commoditized. To stand out from the crowd, vendors are building innovative software portfolios that address common pain points among the developer community.

ABI Research Principal Analyst Reece Hayden has been observing several Mergers & Acquisitions (M&A) abound within the market. These strategic moves are aimed at creating full-stack Artificial Intelligence (AI) platforms, as seen in Qualcomm’s Edge Impulse and STMicroelectronics’ Deeplite acquisitions. Indeed, vendors must now support the entire Machine Learning (ML) pipeline to reduce development friction and accelerate time-to-value.

This Research Highlight explores several key companies that are supporting novel TinyML software integrations. We take a look at notable collaborations, software capabilities, and areas of differentiation.

 

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“TinyML chip vendors are quickly turning to software investment to compete in a saturated, largely homogenous market. A few have mature and monetizable software capabilities (e.g., Syntiant), but most focus on differentiation and value proposition development through software and partnership development (e.g., STMicroelectronics and Qualcomm).” – Reece Hayden, Principal Analyst

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Lattice Semiconductor

Oregon-based Lattice Semiconductor offers TinyML solutions targeting the industrial, automotive, aerospace/defense sectors. Customers can access tool chains, optimized acceleration, Intellectual Property (IP), and production-ready workloads. AI workloads are tailor-made for specific applications, such as Human-Machine Interface (HMI) and defect detection. Lattice partners with technology companies like Neurala and Aizip, while design services are outsourced to third parties.

Syntiant

Based in Irvine, California, Snytiat offers deep learning models that support a variety of chipsets, specifically geared toward vision and audio use cases. Hayden stresses that this helps accelerate model development, facilitates Over-the-Air (OTA) updates, and fosters hardware flexibility. The company’s acquisition of Pilot.ai in 2022 allows it to build hardware-agnostic AI software. Roughly half of Syntiant’s revenue comes from licensing to Qualcomm, Ambarella, and other key players.

Qualcomm

Speaking of Qualcomm, the tech giant is very active in the TinyML space. Its acquisition of Edge Impulse bolstered its Artificial Intelligence of Things (AIoT) capabilities, integrating its MLOps platform. Enterprise customers enjoy vertical-specific solutions, such as industrial object detection. Recently, the company collaborated with Nota AI. This partnership makes edge AI deployments “faster, lighter, and more efficient,” as stated by Chief Technology Officer (CTO) Tae-Ho Kim.

Silicon Labs

Silicon Labs is another key vendor ABI has studied in the TinyML market. Partnerships with Edge Impulse and SensiML enable end-to-end data management and Machine Learning Operations (MLOps) capabilities. To expand its TinyML offerings, Silicon Labs has added Eta Compute for no-code solutions. Moreover, its collaboration with Micro.ai allows users to quickly create a local Microcontroller Unit (MCU), improving performance and security. For example, AI-supported sensors can identify harmful pollutants and other hazards.

Infineon

Infineon has taken an aggressive approach toward edge TinyML AI software development. Case in point, its acquisition of Swedish startup Imagimob supports use cases like auditory event detection, voice control, predictive maintenance, and gesture recognition. Developers enjoy less friction using the platform and can quickly deploy ML models for their desired applications.

NXP

Like many vendors discussed, NXP has enhanced its TinyML offerings via acquisition. In this case, NXP acquired Kinara for US$307 million in February 2025. This strategic move adds an advanced Neural Processing Unit (NPU) edge AI processor, as well as a Software Development Kit (SDK) for model optimization. Concurrently, NXP’s eIQ AI software has been expanded with GenAI Flow and Time Series Studio. Developers can train Large Language Models (LLMs) for things like event detection and set up agentic actions. A core benefit of NXP’s TinyML solutions is the fact that its Retrieval-Augmented Generation (RAG)-based. This framework makes LLMs more secure and efficient, providing context awareness and avoiding AI hallucinations.

STMicroelectronics

STMicroelectronics has established its TinyML focus through the free ST Edge AI Suite, which was launched in 2024. Both in-house tools and strategic partnerships allow the company to simplify edge AI development for users. The platform also offers features like AutoML, neural network optimization, model zoo access, and more. STMicroelectronics takes a forward-looking approach, with its Deeplite acquisition adding IP and talent for model optimization. Furthermore, it supports future “Models-as-a-Service” offerings so that users can customize AI deployments.

 

Get the Full Report

While TinyML chipset vendors are actively supporting software-based functionalities via partnerships and acquisitions, many struggle to realize value. To capitalize on these software assets, Hayden suggests vendors should take the following actions:

• Expand beyond lowest-power hardware
• Target low-power Gen AI workloads
• Invest in optimization layers
• Use Gen AI for model tuning/training and build synthetic datasets
• Adopt hybrid and distributed compute frameworks

For further analysis on the state of TinyML software and how vendors are implementing the best practices above, download ABI Research’s TinyML Chip Vendors Target Differentiation with Full Stack Software Investment report.