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The massive ecosystem of IoT Edge devices brings along with it the most diverse sources of data, delivered across modalities ranging from text, vision, and voice to time series and touch. These data streams now power AI-accelerated device functions such as person detection, speech recognition, voice biometrics, adaptive noise reduction, echo cancellation, machine translation, text summarization, robotic gripping and a host of other multimodal applications. A transformation at this scale can only be enabled through advances in Edge AI silicon architecture and associated software – a combination necessary to unlock ambient intelligence at or near the source of sensor data that extend cloud-based experiences to devices, while delivering privacy and latency benefits.

More efficient AI models and open development tools are helping developers move from concept to deployment faster, turning on-device inference from an interesting possibility into a practical development path.

Edge AI is Enabling a New Innovation Frontier

Conversations about Edge AI often begin with three key considerations: latency, data privacy, and cloud costs. Bringing intelligence to the Edge can reduce round trips to the cloud, improve responsiveness, enhance privacy, and lower dependence on upstream bandwidth. But the bigger opportunity lies in what those advantages make possible.

A device that can see, hear, interpret, and respond locally is not just faster. It is more independent. It can keep functioning when connectivity is poor, reduce the need to send sensitive raw data to the cloud, and deliver experiences that feel natural because decisions are made closer to where the data is created.

Always-On Experiences

Edge device categories like wearables, hearables, smart appliances, automation hubs, and industrial endpoints are often constrained by battery life, thermal limits, size, or some combination of these factors. The right tools can lower the barrier to building ultra-low-power, always-on applications that use ambient sensing to deliver richer AI-enabled experiences that are practical, resilient, and sustainable in daily use.

Product teams need confidence that the systems they build today can adapt and remain relevant even as AI models evolve. The right platform should make experimentation less risky, not more.

Multimodal Systems

Consider what becomes possible when intelligence moves closer to where data is created:

  • A smart home hub can process audio and vision locally to detect events and trigger responses.
  • A wearable or hearable device can combine voice, motion, and environmental awareness while reducing cloud dependence.
  • An industrial cobot can process camera feeds and other sensor data locally so it can respond quickly to activity in a workspace shared with people.

For Edge AI applications to reach their full potential, developers need platforms that support multimodal processing across vision, audio, sensor, and language data, allowing devices to interpret real-world context more fully. Moving from isolated Edge inference to integrated systems that combine compute, connectivity, sensing, and AI into cohesive, intelligent platforms is a shift that will expand what developers can build.

Creating a System that Reduces Edge AI Developer Friction

Fragmented ecosystems, closed toolchains, and difficulty integrating advanced AI into resource-constrained devices can all prevent developers from taking advantage of on-device AI. As AI models, optimization techniques, and frameworks evolve quickly, proprietary stacks can slow broad adoption, creating too much distance between a promising prototype and a production-ready system. If Edge AI remains hard to access, hard to optimize, or locked into fragmented workflows, only a narrow set of teams can use it well.

That is why the real opportunity is not just more capable Edge AI hardware. It is about coming together to establish an ecosystem built on open, standards-based, developer-friendly hardware, software and tooling. Reference platforms with native support for lightweight open models make Edge AI more accessible, lowering barriers to on-device AI innovation and accelerating time to product.

This is the philosophy behind Google Research and Synaptics’ collaboration that has resulted in the Synaptics Coralboard™: a development board designed to enable a new class of intelligent, always-on Edge AI applications where performance, power efficiency, and on-device intelligence define the user experience. Powered by the Synaptics Astra® SL2619 processor, the Coralboard combines a 1 TOPS Torq™ NPU with the industry’s first implementation of the Coral NPU™ from Google Research. Based on a RISC-V architecture, the Coral NPU presents a lightweight, C-programmable frontend designed to handle compute tasks and control flows. Put together, it provides a compact, standards-based developer-ready platform with the interfaces needed for real-world prototyping. The MLIR-based Torq open-source toolchain supports popular machine learning frameworks and models, providing a unified path from experimentation and optimization to deployment. When coupled with native support for Gemma, Google’s family of open models for the Edge, the combined hardware and software stack offers a foundation for building private, efficient and multimodal Edge AI applications.

As AI capabilities expand across everyday devices, the Synaptics Coralboard gives developers a quicker way to build production-grade Edge AI solutions efficiently.

Seizing the Potential of Edge AI

The Edge can become both an extension of experiences that originate in the cloud, as well as a source of enriched ambient context-aware intelligence, securely delivered through low-power devices. With the right mix of open models and open-source tools, scalable future-ready NPU architectures, and developer friendly platforms such as the Coralboard, more teams can start building innovative multimodal experiences on-device. It is a chance to build Edge AI systems that are efficient and private, and more human in the way they respond to the world.

The real opportunity for developers is about having the right toolkits, and true freedom to extend intelligence to where it works best.

SAN JOSE, Calif. – August 21, 2026 – Synaptics Incorporated (Nasdaq: SYNA) today announced that Ken Rizvi has resigned from his position as Chief Financial Officer, effective immediately, to pursue another opportunity. Mr. Rizvi will remain employed in an advisory role through September 30, 2026 to assist with the transition of his responsibilities.

Synaptics has elected not to conduct a search at this time for a successor CFO due to the announcement on June 25, 2026 of the pending merger between Synaptics and onsemi. Rahul Patel, President and CEO, will serve as the principal financial officer through the closing of the merger.

Mr. Kermit Nolan, Synaptics’ former Chief Accounting Officer, is returning as a consultant to advise the internal team through the transition. Over his 20-year tenure, he held several roles at the company, including acting CFO.

“Synaptics has a strong and experienced finance organization, and we are confident in the team’s ability to ensure continuity through the pending transaction,” said Rahul Patel, President and CEO, Synaptics. “Our strategic priorities and focus on driving success across Edge AI and Physical AI markets remain unchanged. I would like to thank Ken for his contributions to our success over the past few years and wish him the best in his new role.”

Processor selection shapes everything that follows in smart device development. Performance, power consumption, connectivity, security and scalability all trace back to decisions made early in the design process.

Modern Internet of Things (IoT) and Edge computing devices keep raising the bar. Capabilities that once defined premium products, such as artificial intelligence (AI) inference and wireless connectivity, now appear across entire product categories.

Engineering teams know the technical requirements. Where things get complicated is balancing those requirements against business realities. So how do you make the right choice?

The first step is to have a structured evaluation framework that aligns processor capabilities with the application’s needs. With this embedded processor selection guide, it’s easier to make the right choice from the start and set products up for success.

Embedded Processors for Smart Devices

Choosing the right embedded processor begins with understanding the differences between microcontrollers (MCU), microprocessors (MPU) and system-on-chip (SoC). Each architecture supports different performance levels, power requirements, software environments and integration capabilities that influence how devices function, develop and scale.

Microcontrollers for Dedicated Tasks

Microcontrollers integrate processing, memory and peripherals into a single chip. Design engineers choose MCUs when applications require deterministic, real-time control that performs specific functions over and over.

Key characteristics of MCUs are:

  • Self-contained architectures
  • Lower power consumption
  • Reduced bill of materials (BOM) costs
  • Simplified development environments
  • High reliability for single-purpose applications

Advantages of using MCUs include:

  • Predictable performance patterns
  • Battery-friendly operation
  • Cost-effective implementation
  • Straightforward integration
  • Proven reliability across deployments

MCUs are found in devices like smart thermostats, environmental sensors, health monitoring wearables, smart home automation devices and industrial monitoring systems.

Applications requiring advanced operating systems, multimedia processing or large-scale AI workloads push beyond MCU capabilities. For these use cases, teams need more computational headroom and software flexibility.

Microprocessors for Complex Operations

Microprocessors for Complex Operations

Complex operations work better with microprocessors as they prioritize computational flexibility and software sophistication. Unlike MCUs, these microprocessors support full operating systems such as Linux and Android, enabling rich software ecosystems and complex multitasking environments.

MPUs feature:

  • Full operating system support
  • External memory and peripherals
  • Greater computational flexibility
  • Complex networking stacks
  • Advanced graphical interfaces
  • Rich software ecosystems

Typical applications include:

  • Smart displays with touch interfaces
  • Industrial gateways managing multiple protocols
  • Robotics systems with vision processing
  • Connected appliances running apps
  • Human-machine interfaces with rich graphics

Software requirements often determine architectural suitability before hardware specifications are considered. Operating system compatibility constraints can narrow down processor options early, as teams must consider not only current software needs but also future application requirements.

SoC for Maximum Integration

SoC combines multiple system functions into a single platform. Engineers choose SoC architectures when design objectives favor consolidation over discrete component architectures.

The benefits of SoC integration include:

  • Simplified hardware development
  • Lower BOM costs
  • Smaller device footprints
  • Improved power efficiency
  • Decreased inter-chip communication overhead

Common integrated subsystems include:

  • Application processors
  • Microcontroller cores
  • Memory controllers
  • Connectivity subsystems
  • Graphics engines
  • Specialized accelerators

Smart home devices, industrial equipment, connected displays and advanced IoT products often benefit from strong integration. But not every application needs maximum consolidation.

Engineering teams should evaluate integration levels against specific application requirements rather than assuming that more integration always delivers better outcomes. Some designs perform better with discrete components that allow independent optimization of subsystems.

Key Evaluation Criteria for Embedded Processors

After identifying the appropriate processor category, engineering teams face the challenge of evaluating the factors that determine real-world performance and product success. Selection criteria should address application requirements, power budgets, AI workloads, connectivity needs, security posture and future expansion plans.

  • Accelerates onsemi’s evolution, building on its strength in power and sensing to become a leading provider of intelligent systems — expanding from AI data centers into Physical AI
  • Increases onsemi’s total addressable market by $30 billion to $243 billion by 2030
  • Positions onsemi at the intersection of Power, Sense, Connected Compute and Control — the four pillars of Physical AI — which enable machines to sense, decide, act and adapt in the physical world
  • Would combine complementary portfolios to drive significant customer value and deepen customer engagements

SCOTTSDALE, Ariz. & SAN JOSE, Calif. – June 25, 2026 – onsemi (Nasdaq: ON) and Synaptics Incorporated (Nasdaq: SYNA) today announced they have entered into a definitive agreement under which onsemi has agreed to acquire Synaptics in an all-stock transaction, representing a total enterprise value of approximately $7 billion. The transaction value reflects a fixed exchange ratio of 1.350 shares of onsemi common stock for each Synaptics share and represents an approximately 19% premium to the volume weighted average closing prices of onsemi and Synaptics over the last 10 trading days.

The combination would accelerate onsemi’s evolution toward global leadership in intelligent systems. By adding Synaptics’ differentiated Edge AI compute franchise and strong portfolio of human-machine interface and wireless connectivity solutions, onsemi is expected to extend its capabilities beyond power and sensing to intelligent systems, delivering greater value to a broad range of end markets. Building on onsemi’s expertise in automotive, industrial and AI data center, the combined platform is intended to position onsemi at the center of Physical AI, with the potential to expand onsemi’s TAM by $30 billion to $243 billion by 2030.

“As artificial intelligence moves beyond the cloud and into the physical world, including automotive and industrial, the next phase of innovation will depend on systems that can sense, decide, act and adapt in real time,” said Hassane El-Khoury, President and CEO of onsemi. “This shift towards Physical AI will require Power, Sense, Connected Compute and Control to work together seamlessly. The addition of Synaptics helps position onsemi at the intersection of these four pillars, enabling us to capture a significantly larger AI opportunity that extends beyond AI data center and into edge applications. This transaction would add immediate connected compute capabilities, expand our software and ecosystem reach and position onsemi to deliver greater value as customers increasingly seek intelligent systems.”

“Today’s announcement marks an important step in accelerating Synaptics’ growth and leadership in Edge AI and Physical AI,” said Rahul Patel, Synaptics President and CEO. “Together with onsemi, we will combine Synaptics’ strengths in AI-native compute, connectivity, and human-machine interface with onsemi’s leadership in intelligent power and sensing to offer customers integrated solutions and development platforms across every layer of the Edge AI stack, deepening customer engagement and expanding across a greater total addressable market. The all-stock structure allows our shareholders to participate in the compelling growth and value creation opportunities ahead, and I look forward to working with the onsemi leadership team to help realize the full value of this combination.”

 

Compelling Strategic and Financial Rationale

The combination is expected to deliver substantial value:

  • Enables capabilities from AI Infrastructure to Physical AI: onsemi is already well-positioned across the AI infrastructure ecosystem, from the energy grid to the data center core. This transaction is expected to extend that reach to the intelligent edge, enabling onsemi to address additional end markets while enhancing its capabilities to become a provider of integrated, system-level solutions across Power, Sense, Connected Compute and Control. This compelling combination would enable systems that can sense, decide, act and adapt in real time across Physical AI applications, including autonomous driving, robotics, and AR/VR.
  • Adds a proven, scalable Edge AI connected compute platform to onsemi: Synaptics’ Astra platform combines purpose-built AI processors and NPUs for multimodal intelligence with an industry-leading wireless connectivity portfolio spanning Wi-Fi, Bluetooth and GPS and a full open-source software stack for rapid deployment.
  • Complementary portfolios designed to unlock significant revenue growth with scale: The combination of two highly complementary portfolios would allow onsemi to accelerate its innovation and product roadmap to capture higher dollar content per platform while fostering deeper long-term customer engagement. This is anticipated to increase onsemi’s exposure to higher-value, differentiated system solutions with embedded IP and software, supporting improved mix, margin expansion and durable growth.
  • Attractive financial profile: The transaction is expected to be accretive to non-GAAP EPS within 18 months of closing, with an expected $200 million in annual synergies and gross margins consistent with onsemi’s long-term financial model. onsemi remains committed to maintaining its existing capital return policy during the pendency period.

     

Transaction Details

Under the terms of the agreement, which has been unanimously approved by the Boards of Directors of both companies, Synaptics stockholders will receive 1.350 shares of onsemi common stock for each share of Synaptics common stock held at the time of closing, implying pro forma ownership of approximately 12% for Synaptics stockholders on a fully diluted basis.

As part of the transaction, one member of the Synaptics Board of Directors is expected to join onsemi’s Board of Directors.

The transaction is expected to close in mid-2027, subject to approval by Synaptics stockholders, the receipt of required regulatory approvals and other customary conditions.

onsemi and Synaptics Reiterate Previously Provided Financial Outlooks

As part of today’s announcement, onsemi is reiterating its financial outlook for the second fiscal quarter of 2026 provided on May 4, 2026. Synaptics is reiterating its financial outlook for the fiscal fourth quarter of 2026 provided on May 7, 2026.

Conference Call and Webcast Information

onsemi will host a conference call for the financial community at 5:00 p.m. Eastern Daylight Time (EDT) on June 25, 2026, to discuss the transaction announcement. A live webcast and related presentation materials will be available on onsemi’s IR site at http://www.onsemi.com. The webcast replay and presentation will be available following the call. Investors and interested parties can also access the conference call by pre-registering here.

Advisors

Morgan Stanley served as lead financial advisor to onsemi. J.P. Morgan Securities LLC also served as a financial advisor and Skadden, Arps, Slate, Meagher & Flom LLP served as legal counsel to onsemi. Qatalyst Partners acted as exclusive financial advisor and Baker McKenzie served as legal counsel to Synaptics.

 

From the early days of the Internet of Things (IoT), connected devices have been highly effective at sensing and processing real-world inputs. But as AI workloads grow more demanding and real-time responsiveness becomes critical, the value is no longer in collecting data—it’s in acting on it instantly. This shift is pushing AI-native compute out of the cloud and onto the Edge, where decisions can be made in the moment.

Imagine a tennis racket that doesn’t just track your swing, but analyzes it in real time and provides feedback on the spot. That level of responsiveness requires AI processing on the device — exactly what the Synaptics® Astra™ SRW1500 Series single-chip AI MCU platform is designed to deliver. As Edge AI continues to mature, IoT is evolving from a data-gathering paradigm into a continuous intelligence loop where devices sense, respond, and improve over time, unlocking new capabilities across consumer electronics, smart appliances, and more.

Powerful Edge AI with Integrated Wireless Connectivity

To keep up with — and innovate — at the edge, it’s essential to do more than connect devices to the cloud for intelligence. Real-time responses to data require embedded intelligence in connected devices. As the IoT continues to scale, power budgets will keep shrinking, and latency requirements will get tighter. A single-chip AI MCU platform addresses these challenges while also improving device intelligence and security.

For IoT systems that demand this kind of real-time inference, low-latency responsiveness, and advanced wireless connectivity, the SRW1500 Series is the answer: a single-chip AI MCU platform that integrates an Arm® Cortex®-M52 processor, 1 MB of on-chip SRAM, an Arm® Ethos™-U55 NPU for efficient on-device inferencing, and Wi-Fi® 7 connectivity into a single device — purpose-built for real-time Edge AI inference in connected IoT applications.

This tightly integrated architecture delivers optimized Edge AI performance for always-on sensing, contextual awareness, and on-device decision-making. By coupling the MCU and AI architecture at the silicon level, the platform enables advanced workloads like voice trigger detection, sound event classification, and AI-enhanced Wi-Fi® sensing for presence and motion detection.

The SRW1500 Series balances compute efficiency, low power consumption, and system cost optimization, while integrating multi-protocol wireless connectivity within a single device, making it a perfect fit for intelligent edge applications.

Benefits of a Single-Chip AI MCU Platform for Connected IoT

A single-chip AI MCU platform delivers compounding advantages for connected IoT design.

First and foremost, it simplifies system architecture by removing chip-to-chip communication between separate components, unifying MCU, NPU (for AI), and wireless radios into a single, tightly coupled platform. This on-chip AI inference reduces latency, enables faster decision-making, and lowers overall power consumption.

As mentioned, single-chip architecture reduces latency by keeping sensor data, AI inference, and the resulting action all on the device. Compared to sending data to the cloud, this allows for real-time decision-making.

Naturally, multiple chips and interconnect components lead to higher costs and larger PCB footprints. They can also add bottlenecks at chip interfaces due to limited coordination between compute and connectivity. A single chip is not only more cost effective as an integrated device, but it’s more efficient with AI, MCU, and connectivity co-designed to optimize data paths and scheduling.

Security is equally critical. The SRW1500’s single-chip architecture raises the bar at the silicon level, reducing the attack surface that exists when data moves across chip boundaries in multi-chip designs. With security and compute co-located on one device, sensitive inference data stays on-device, supporting compliance with increasingly stringent IoT security standards. The SRW1500 includes a Secure Island with PSA Level 3 certification, simplifying security validation to a single auditable platform rather than multiple discrete components.

Reducing Design Complexity with a Unified AI MCU SDK

One of the greatest advantages of moving to a single-chip MCU is how it streamlines designs to shrink development times and associated costs. By removing chip-to-chip interface design and offering a unified, open SDK, engineers gain a consistent developer experience across standalone and connected compute implementations. This frees them to focus on their core specialties without being slowed by compute or connectivity concerns.

For example, smart fridge designers can prioritize end-user experiences like air quality applications that can detect spoilage, knowing they have an AI-native MCU that includes connectivity to enable richer, more personalized intelligence managed on-device and across IoT networks. The SRW1500 Series SDK supports FreeRTOS™ and Zephyr®, and provides a developer-friendly environment with pre-integrated firmware and middleware for AI-accelerated connectivity stacks.

A Unified Platform for Faster, Smarter Edge AI Development

As AI continues to drive innovation, consumers expect new capabilities across smart homes, appliances, home security, and consumer electronics. Edge AI is accelerating this shift by enabling developers to create smarter, more responsive, and differentiated user experiences— from predictive industrial sensing to context-aware smart home automation. The potential of Edge AI is limited only by developers’ imaginations.

Success in this space requires more than just Edge AI capability—it depends on efficient tools that simplify model creation, optimization, and deployment. The SRW1500 platform delivers this combination, helping developers accelerate innovation while reducing complexity.

By delivering compute and connectivity in a single chip—and from a single vendor—engineers can reduce complexity and focus on bringing these innovations to market faster. With best-in-class processing, the SRW1500 extends the Astra MCU portfolio as one of the industry’s first single-chip AI MCU platforms with integrated Wi-Fi® 7 connectivity, supported by open, developer-friendly software for continuous AI at the far edge of the IoT network. The SRW1500 provides engineering teams with a faster, more efficient path to intelligent edge products—backed by the Astra portfolio’s proven AI MCU architecture.

Ready to see SRW1500 in action? Watch the overview video to see how AI-native compute and integrated connectivity come together at the Edge.

Experience it live at Computex Taipei, Nangang Exhibition Center Hall 2, Suite #X0022 — or explore the full SRW1500 Series on the Synaptics website.

SAN JOSE, Calif., Mar 10, 2026 — Synaptics Incorporated (Nasdaq: SYNA), today announced the SYN765x, an AI-native wireless solution that redefines Edge intelligence. As an industry-leading single-chip device combining AI-optimized compute with integrated Wi-Fi® 7, the SYN765x is designed to bring scalable, real-time intelligence directly to smart appliances, home automation systems, and Industrial IoT (IIoT) applications.

SYN765x integrates Wi-Fi 7, Bluetooth® LE 6.0, and Thread/Zigbee across 2.4, 5, and 6 GHz bands. Dedicated on-chip acceleration supports predefined AI-native control and signal-processing functions, reducing host processor load, while meeting strict latency and power requirements.

Built for high-performance wireless connectivity, the SYN765x pairs essential compute as a convenient, on-chip companion for smart devices at the Edge. Its single-chip integration significantly reduces system space requirements, simplifies design, and offers both engineering flexibility and cost savings.

By bringing advanced Wi-Fi 7 capabilities to low-power designs, SYN765x helps enable lower latency, seamless band switching, fast and secure reconnections, and access to the interference-free 6 GHz spectrum. Until now, power and cost constraints have limited adoption of the latest Wi-Fi standards in embedded systems. This solution meaningfully reduces these barriers, accelerating next-generation Wi-Fi adoption across Edge IoT devices.

The SYN765x also enables advanced wireless sensing, including presence detection, motion tracking, and proximity awareness using standard Wi-Fi and Bluetooth signals. For Wi-Fi sensing, it combines high-accuracy Channel State Information (CSI) extraction with on-device machine learning. This differentiated architecture is designed to deliver high accuracy and reliability, supporting a broader range of sensing use cases compared to other connectivity solutions in its class.

In addition, Bluetooth Channel Sounding enables accurate, power-efficient distance measurements under typical operating conditions — offering an alternative to more expensive technologies such as mmWave radar or ultra-wideband. Integrated logic, DSP, and NPU resources provide a high-performance platform for embedded Edge AI workloads, while AI-driven contextual awareness enhances power efficiency.

The SYN765x supports flexible deployment models. It can operate as a co-processor alongside a host application processor or MCU, or function in standalone and host-less configurations thanks to its generous on-chip memory and processing resources. By eliminating the need for a separate microcontroller in many designs, the SYN765x significantly reduces system complexity and cost—offering a new class of high-performance, battery-powered IoT devices.

“Intelligence at the Edge demands uncompromising wireless performance,” said Vikram Gupta, SVP & GM, Edge Compute & Connectivity Division, Synaptics. “SYN765x extends our leadership in wireless technology by integrating Wi-Fi® 7, BLE, Thread/Zigbee with AI-native processing. This simplifies system design, lowers power and cost barriers, and accelerates adoption of next-generation wireless across consumer, industrial, and enterprise IoT markets.”

SYN765x Features at a Glance:

  • Wi-Fi 7 – Tri-band, 1×1 20 MHz, Bluetooth 6.0, Thread
  • Concurrent operation of Wi-Fi, Bluetooth, and Thread
  • Sustained 20 Mbps throughput up to 200m
  • AI-enabled algorithms supporting Wi-Fi Sensing, Bluetooth Channel Sounding, Sound Event Detection
  • Triple combo integration. PCB footprint < 100mm2
  • QFN package enabling non-HDI PCB
  • Integrated LNA, PA & T/R switches
  • Up to 25% lower RBOM compared to comparable multi-chip solutions
  • Extensive peripheral support: UART, SPI, SDIO, I2C, I2S, USB, GPIO, ADC, DAC, PDM

Global customers and partners count on Synaptics to power the next generation of connected, AI-enabled IoT applications.

Gilles Drieu, Chief Technology Officer, ADT
“Connectivity is at the heart of everything we deliver to our customers. Our partnership with Synaptics enables us to build security solutions that are more reliable, intelligent, and responsive. Their portfolio of connectivity solutions gives us the flexibility and performance we need to design next-generation systems that protect homes more effectively, while remaining simple to deploy and manage.”

Janet Wei, CEO, Ampak
“Synaptics has been a trusted technology partner for many years, and our longstanding collaboration has been instrumental in advancing connectivity solutions across a wide range of applications. Together, we’ve consistently pushed the boundaries of performance, integration, and reliability, enabling customers worldwide to deploy robust, scalable wireless solutions and reinforcing our shared vision for the future of connected devices.”

Spencer Maid, President and CEO, Origin AI
“Synaptics brings world-class AI and connectivity capabilities that perfectly complement Origin’s AI Sensing platform. Together, we’re unlocking powerful new use cases that transform everyday environments into smarter, more adaptive spaces.”

Andrew Zignani, Senior Research Director, ABI Research, Strategic Technologies Team
“Artificial intelligence has been migrating out from data centers throughout the network, and now the Edge is truly ripe for local AI. From factory equipment, where reliability and security are key, to consumer electronics, where price sensitivity and privacy is acute, to wearables, where weight, size, and battery life are critical, Synaptics’ latest offering effectively balances and optimizes computing and reliable, future proof connectivity to better serve the evolving requirements and growing diversity of product types found at the network Edge.”

Mohit Agrawal, Global Practice Head for Edge AI and IoT, Counterpoint Research
“The shift from cloud-centric IoT architectures to real-time Edge intelligence is accelerating, particularly as latency, privacy, and bandwidth efficiency become critical design priorities. Integrating AI acceleration with Wi-Fi 7 on a single SoC represents an important step forward for the AIoT ecosystem. As Wi-Fi 7 adoption expands over the next several years, solutions that tightly couple on-device inference with high-performance connectivity will be well positioned to support next-generation smart home, industrial, and infrastructure applications.”

Availability
The Synaptics SYN765x solution is expected to begin sampling in the second calendar quarter of 2026, with production targeted at the last calendar quarter of 2026. Development kits are expected to be available for sampling in calendar Q2 2026.

Edge AI, Brought to Life at Embedded World

Embedded World 2026 made one thing clear: AI is no longer confined to the cloud—it’s moving decisively onto the device. Across our demos and conversations, a consistent theme emerged: intelligence is shifting closer to where data is created—into devices, environments, and the physical world.

From smart homes to industrial systems and a wide range of emerging robotics applications, the focus is evolving from what AI can do to how efficiently, responsively, and seamlessly it operates at the Edge.

From Edge Intelligence to Real-World Awareness

Edge AI is evolving into context-aware, real-world intelligence. Systems are beginning to not just process data, but also to understand context and respond in real time.

At Embedded World, we brought this to life through integrated platforms that sense, process, and act—demonstrating how AI is transitioning from a technical capability to a tangible user experience across real-world applications.

Smart Homes: SYN765x Connectivity Platform

In smart homes, AI is enabling devices to detect events, automate responses, and enhance security, while preserving privacy through local processing.

Our latest SYN765x solution integrates Wi-Fi® 7, Bluetooth® 6.0, and embedded AI compute into a single solution. The result: faster decision-making, reduced system complexity, and built-in security—bringing real-time intelligence directly into the home.

Edge AI Audio MCUs: Synaptics Astra™ SR80

Audio devices are becoming more intelligent and responsive. From headsets to conferencing systems, AI enables real-time voice recognition, noise suppression, and contextual audio processing.

The Synaptics Astra SR80 family is designed for always-on, low-power intelligence — delivering adaptive, personalized audio experiences that respond almost instantly to users and their environments.

Advancing the Ecosystem: Coral and Google Collaboration

We also showcased the Synaptics Coral Dev Board, highlighting how advanced AI workloads can run directly on Edge devices. Powered by Astra SL2610 and Synaptics’ Torq™ NPU—alongside the Coral NPU by Google Research—the dev board enables efficient, on-device inference for both generative and perception-based AI.

Pre-configured with the Gemma™ model and supported by an open, MLIR-based toolchain, it provides a streamlined path from prototyping to production—making Edge AI more practical and accessible across smart home, industrial, wearables, and hearables applications.

Coral Board

Together, these demos illustrate the broader transition: from isolated Edge inference to systems that combine processing, connectivity, sensing, and AI into cohesive, production-grade applications.

Why Edge AI Changes Everything

Bringing AI to the Edge fundamentally transforms system performance and scalability. It enables:

  • Real-time responsiveness with ultra-low latency
  • Enhanced privacy through local data processing
  • Reduced reliance on cloud infrastructure
  • Greater power efficiency for embedded systems
  • Increased autonomy, allowing devices to operate independently

These benefits are accelerating the shift toward distributed intelligence, where processing is embedded across connected devices rather than centralized in the cloud.

Building an Open Ecosystem for Edge AI Innovation

As Edge AI adoption accelerates, developer accessibility becomes critical.

Synaptics is focused on enabling innovation through support for open frameworks and toolchains, including evolving compiler technologies, exemplified by collaboration with partners such as Google Research, to expand AI capabilities at the Edge.

This approach helps reduce barriers to development and supports a more scalable ecosystem—allowing developers to build, deploy, and iterate more quickly.

The Future: Intelligent, Connected, Everywhere

AI is rapidly becoming a foundational capability across embedded systems.

At the center of this evolution is the shift toward integrated platforms that combine compute, connectivity, and sensing—regardless of the application.

Synaptics is enabling this transition by helping bring intelligence to the Edge, where it can deliver the greatest impact.

Looking Forward

Thank you to everyone who visited Synaptics at Embedded World.

If we didn’t connect during the show, we welcome the opportunity to continue the conversation.

Because, as AI continues to evolve, one thing is clear:

Intelligence is most powerful when it’s embedded, efficient, and exactly where it needs to be.

SAN JOSE, Calif., Mar 10, 2026 — Synaptics Incorporated (Nasdaq: SYNA), today announced the SYN765x, an AI-native wireless solution that redefines Edge intelligence. As an industry-leading single-chip device combining AI-optimized compute with integrated Wi-Fi® 7, the SYN765x is designed to bring scalable, real-time intelligence directly to smart appliances, home automation systems, and Industrial IoT (IIoT) applications.

SYN765x integrates Wi-Fi 7, Bluetooth® LE 6.0, and Thread/Zigbee across 2.4, 5, and 6 GHz bands. Dedicated on-chip acceleration supports predefined AI-native control and signal-processing functions, reducing host processor load, while meeting strict latency and power requirements.

Built for high-performance wireless connectivity, the SYN765x pairs essential compute as a convenient, on-chip companion for smart devices at the Edge. Its single-chip integration significantly reduces system space requirements, simplifies design, and offers both engineering flexibility and cost savings.

By bringing advanced Wi-Fi 7 capabilities to low-power designs, SYN765x helps enable lower latency, seamless band switching, fast and secure reconnections, and access to the interference-free 6 GHz spectrum. Until now, power and cost constraints have limited adoption of the latest Wi-Fi standards in embedded systems. This solution meaningfully reduces these barriers, accelerating next-generation Wi-Fi adoption across Edge IoT devices.

The SYN765x also enables advanced wireless sensing, including presence detection, motion tracking, and proximity awareness using standard Wi-Fi and Bluetooth signals. For Wi-Fi sensing, it combines high-accuracy Channel State Information (CSI) extraction with on-device machine learning. This differentiated architecture is designed to deliver high accuracy and reliability, supporting a broader range of sensing use cases compared to other connectivity solutions in its class.

In addition, Bluetooth Channel Sounding enables accurate, power-efficient distance measurements under typical operating conditions — offering an alternative to more expensive technologies such as mmWave radar or ultra-wideband. Integrated logic, DSP, and NPU resources provide a high-performance platform for embedded Edge AI workloads, while AI-driven contextual awareness enhances power efficiency.

The SYN765x supports flexible deployment models. It can operate as a co-processor alongside a host application processor or MCU, or function in standalone and host-less configurations thanks to its generous on-chip memory and processing resources. By eliminating the need for a separate microcontroller in many designs, the SYN765x significantly reduces system complexity and cost—offering a new class of high-performance, battery-powered IoT devices.

“Intelligence at the Edge demands uncompromising wireless performance,” said Vikram Gupta, SVP & GM, Edge Compute & Connectivity Division, Synaptics. “SYN765x extends our leadership in wireless technology by integrating Wi-Fi® 7, BLE, Thread/Zigbee with AI-native processing. This simplifies system design, lowers power and cost barriers, and accelerates adoption of next-generation wireless across consumer, industrial, and enterprise IoT markets.”

SYN765x Features at a Glance:

  • Wi-Fi 7 – Tri-band, 1×1 20 MHz, Bluetooth 6.0, Thread
  • Concurrent operation of Wi-Fi, Bluetooth, and Thread
  • Sustained 20 Mbps throughput up to 200m
  • AI-enabled algorithms supporting Wi-Fi Sensing, Bluetooth Channel Sounding, Sound Event Detection
  • Triple combo integration. PCB footprint < 100mm2
  • QFN package enabling non-HDI PCB
  • Integrated LNA, PA & T/R switches
  • Up to 25% lower RBOM compared to comparable multi-chip solutions
  • Extensive peripheral support: UART, SPI, SDIO, I2C, I2S, USB, GPIO, ADC, DAC, PDM

Global customers and partners count on Synaptics to power the next generation of connected, AI-enabled IoT applications.

Gilles Drieu, Chief Technology Officer, ADT
“Connectivity is at the heart of everything we deliver to our customers. Our partnership with Synaptics enables us to build security solutions that are more reliable, intelligent, and responsive. Their portfolio of connectivity solutions gives us the flexibility and performance we need to design next-generation systems that protect homes more effectively, while remaining simple to deploy and manage.”

Janet Wei, CEO, Ampak
“Synaptics has been a trusted technology partner for many years, and our longstanding collaboration has been instrumental in advancing connectivity solutions across a wide range of applications. Together, we’ve consistently pushed the boundaries of performance, integration, and reliability, enabling customers worldwide to deploy robust, scalable wireless solutions and reinforcing our shared vision for the future of connected devices.”

Spencer Maid, President and CEO, Origin AI
“Synaptics brings world-class AI and connectivity capabilities that perfectly complement Origin’s AI Sensing platform. Together, we’re unlocking powerful new use cases that transform everyday environments into smarter, more adaptive spaces.”

Andrew Zignani, Senior Research Director, ABI Research, Strategic Technologies Team
“Artificial intelligence has been migrating out from data centers throughout the network, and now the Edge is truly ripe for local AI. From factory equipment, where reliability and security are key, to consumer electronics, where price sensitivity and privacy is acute, to wearables, where weight, size, and battery life are critical, Synaptics’ latest offering effectively balances and optimizes computing and reliable, future proof connectivity to better serve the evolving requirements and growing diversity of product types found at the network Edge.”

Mohit Agrawal, Global Practice Head for Edge AI and IoT, Counterpoint Research
“The shift from cloud-centric IoT architectures to real-time Edge intelligence is accelerating, particularly as latency, privacy, and bandwidth efficiency become critical design priorities. Integrating AI acceleration with Wi-Fi 7 on a single SoC represents an important step forward for the AIoT ecosystem. As Wi-Fi 7 adoption expands over the next several years, solutions that tightly couple on-device inference with high-performance connectivity will be well positioned to support next-generation smart home, industrial, and infrastructure applications.”

Availability
The Synaptics SYN765x solution is expected to begin sampling in the second calendar quarter of 2026, with production targeted at the last calendar quarter of 2026. Development kits are expected to be available for sampling in calendar Q2 2026.

One Platform – Infinite Possibilities

Devices are becoming smarter, more capable, and more distributed, but the way we design them has not kept pace. For engineers, that progress increasingly brings tradeoffs: latency bottlenecks, rising power demands, and fragmented system architectures that complicate even well-understood designs. As functionality increases, so does the difficulty of fitting multiple analog and digital components onto a single board, while meeting strict size, weight, and power requirements.

The goal isn’t just integration. It’s better outcomes. Integrating processing and connectivity helps reduce system complexity, improve reliability, strengthen security, and simplify the development experience for design teams. It accelerates time to market and supports AI-capable products across applications ranging from consumer devices to industrial and physical AI systems.

Advances in mixed-signal design are making this level of integration practical at scale. By integrating a wireless chip and a microcontroller, engineers can eliminate chip-to-chip interconnect complexity, thereby simplifying board layouts, improving power management, and making system integration faster and more efficient.

The Happy Marriage of Connectivity and Compute

Bringing together connectivity and processing changes how design decisions are made early in the product lifecycle. When core system functions work together, teams can simplify architecture choices from the outset and reduce the number of variables that typically slow progress.

Traditionally, developers have had to account for board layout while coordinating both hardware and software development across separate components and teams. That level of coordination increases design complexity, introduces roadblocks, and can slow development and time to market. An integrated solution removes much of that friction.

Long-Term Benefits and Efficiencies of Integration

Integrating connectivity and compute has benefits beyond the engineering and manufacturing phase. Over the lifetime of a product, integration helps reduce power consumption, lower device weight, and decrease overall system cost. At scale, even small reductions in size, mass, and power can translate into meaningful savings across production, shipping, and years of deployment.

These efficiencies matter across a wide range of IoT applications, including smart appliances, home and industrial automation, and home security. For products that must meet stringent energy requirements, integration can make it significantly easier to achieve electricity consumption targets.

Reliable wireless performance over longer distances is another critical factor. Connectivity can be power- and workload-aware. Products such as washers, dryers, and thermostats are often installed far from a router, where inconsistent connectivity can undermine the overall experience. More efficient system designs enable robust and reliable wireless connections in these real-world environments.

Bring Processing and Connectivity Together with Synaptics

As leaders in connectivity, Synaptics focuses on helping engineers improve designs through high-performance wireless solutions that deliver strong rate, range, and reliability. That expertise drives our approach to integrated platforms, where connectivity and processing are designed to work together from the start.

As Wi-Fi 7 brings higher speeds and lower latency to the IoT, Synaptics connectivity solutions are built to help engineers take advantage of these capabilities while simplifying system design and improving overall connectivity.

When Wi-Fi® 7 makes headlines, the focus is often on faster home routers and next-generation smartphones. Those improvements are real—however, they’re only part of the story. The true transformation enabled by Wi-Fi 7 will be felt in the Internet of Things (IoT), which now includes more than 21 billion connected devices worldwide.

With capabilities such as Multi-Link Operation (MLO), deterministic latency, more efficient use of spectrum, and dramatically higher throughput and range, Wi-Fi 7 is emerging as the foundational wireless standard for the next generation of connected Edge IoT devices. For engineers designing these Edge IoT products, the conversation is no longer about if Wi-Fi 7 should be adopted, but about how quickly it can be integrated to maintain performance, scalability, and competitive advantage.

How Wi-Fi 7 Will Redefine What’s Possible for IoT

As Wi-Fi 7 moves beyond traditional consumer devices, its role becomes even more critical. In Industrial IoT (IIoT) environments, smart homes, and Edge applications, wireless connectivity must perform reliably despite RF interference, latency constraints, and constantly changing operating conditions.

To put the challenge in perspective, the average home already supports more than 20 connected devices. Now scale that reality to dense urban settings, industrial environments, or smart infrastructure deployments, and the pressure on wireless networks becomes obvious. It’s no coincidence that technologies like 5G are built to handle massive device density in limited spaces. Edge IoT design engineers and developers face a similar challenge: delivering consistent, high-quality connectivity in environments that are increasingly crowded and unpredictable. This is where Wi-Fi 7 changes the equation. The examples below illustrate how its advanced capabilities are designed to meet these real-world connectivity demands head-on.

320 MHz Channels Unlock a New Class of Wi-Fi Performance

Think of Wi-Fi 7 as widening the digital freeway. By expanding channel bandwidth up to 320 MHz—double that of Wi-Fi 6—it allows far more data to move at once. The result is higher throughput, less congestion, and more consistent performance. In dense IoT device environments, this added capacity translates into faster data transfers, greater predictability, and the efficiency needed to scale as Edge IoT device counts continue to rise.

Always-On Connectivity via Multi-Link Operation (MLO)

When timing matters, IoT devices need connectivity that is both reliable and predictable. The Time-Sensitive Multi-Link Operation (TMLO) capability that comes with Wi-Fi 7 enables devices to communicate simultaneously across multiple frequency bands—2.4 GHz, 5 GHz, and 6 GHz—minimizing the effects of interference and congestion.

Unlike earlier Wi-Fi generations that pause to switch bands or reroute traffic when conditions change, Wi-Fi 7 keeps multiple links active at once. Data flows can shift instantly to the best-performing link, ensuring smooth transitions and continuous delivery.

By distributing traffic across bands and providing built-in redundancy when one frequency is degraded, TMLO delivers lower latency and more consistent timing—key requirements for time-sensitive IoT applications.

Higher Throughput, Greater Efficiency with 4K QAM

With 4K Quadrature Amplitude Modulation (QAM), a new function introduced with Wi-Fi 7, more data can be encoded into each transmission. In environments with many devices and limited channel availability, faster transmissions free up airtime for others and improve overall network efficiency. Completing transmissions sooner also allows devices to return to lower power states quicker, reducing power consumption. The effect is like modern electric vehicles, delivering higher performance while operating more efficiently.

Better Security with Mandatory WPA3

With Wi-Fi 7, support for the WPA3 security protocol is mandatory, not optional. WPA3 strengthens protection against brute-force and offline dictionary attacks through Simultaneous Authentication of Equals (SAE). It also delivers improved per-device encryption and stronger session key management, limiting lateral movement across the network and reducing the risk of compromised IoT devices being used as attack pivots.

A Stronger Foundation for Next-Generation Wireless

Wi-Fi 7 brings together higher performance, greater efficiency, and stronger baseline security to create a more capable wireless platform. The result is faster speeds, more predictable behavior, and built-in protections—without tradeoffs.

These advances enable more consistent performance for ultra-low-latency and bandwidth-intensive applications, including AR and VR, 4K video streaming and OTT services, premium audio for soundbars and home theater systems, gaming consoles, and security cameras.

Choosing the Right Wi-Fi 7 Solution

Wi-Fi 7 significantly expands what wireless connectivity can support—but unlocking its full potential requires a thoughtful, platform-level design. Performance, efficiency, size, and security must be considered together as connectivity and processing are integrated into increasingly constrained Edge IoT systems.

At Synaptics, these principles guide how we design and deliver Wi-Fi 7 solutions for Edge IoT applications.

Performance and Efficiency

For IoT designers, success with Wi-Fi 7 means delivering higher throughput and lower latency without increasing power consumption. Achieving this balance depends not only on the capabilities of the connectivity solution and MCU, but on how effectively they are designed to work together as a unified system.

Size and Weight

Simplified system architectures create both technical and economic advantages. Higher levels of integration improve performance, power efficiency, and reliability, while reducing Bill of Materials (BOM) complexity and production cost. Tighter integration also enables smaller, lighter designs—critical for space- and weight-constrained devices such as AR and VR glasses.

On-Chip Security

While Wi-Fi 7 strengthens security at the transport layer, it does not protect firmware or replace network segmentation and firewall-based defense. To fully realize its security benefits, designers should choose platforms with built-in, on-chip security to help safeguard firmware, credentials, and overall system integrity.

AI-Native Edge Processing and Connectivity

The convergence of low-power Edge AI–enabled microcontrollers and advanced Wi-Fi 7 connectivity marks a new frontier for intelligent devices. MCUs running applications and on-device AI rely on tightly integrated connectivity modules or SoCs to get the most out of Wi-Fi 7 capabilities.

When processing and connectivity are designed to work as a single, unified platform, designers can unlock higher performance, lower latency, and greater efficiency—doing more with less. This is where the next generation of Wi-Fi 7 Edge IoT devices will take shape, enabled by platforms built for seamless integration.