MaxLinear, Inc. (Nasdaq: MXL), a global leader in broadband access and gateway solutions, recently unveiled MaxAI™, a groundbreaking hardware-optimized, use-case aware, native-ML technology framework designed to provide enhanced user experience, improve quality of service (QoS), and lower support costs for Multiple System Operators (MSOs).
According to Industry sources, with ubiquitous Wi-Fi across the globe, hundreds of millions of people rely on it for their daily tasks. Work From Home (WFH) has made that reliance even stronger, making the job of MSOs and service providers even more challenging.
Industry sources further added that a Poor Wi-Fi experience translates into a bad client experience and surged support budget for the service provider. While Wi-Fi speed is one component of the experience, it does not always translate into overall satisfaction.
Industry sources further confirmed that at a time when MSOs and end clients are asking for solutions to help mitigate some of the pain points of the access and connectivity ecosystem, MaxAI offers the AI framework, scalability, and flexibility to craft compelling solutions that are designed to meet the requirement of current modern era’s most demanding multi-user applications like XR, video conferencing, and multi-player gaming. The integration of MaxAI into MaxLinear’s AnyWAN™ solutions significantly enhances network performance, security, issue triage, and diagnostics and also improves user experience by intelligently adapting to usage patterns while still adhering to application KPIs.
The uniqueness and complexity of people’s homes and businesses, integrated with the diversity of Wi-Fi devices, create a unique problem where a one-size-fits-all solution does not work. Each application has its own throughput, latency, and concurrency requirements that need to be addressed individually. The home gateway and router need the intelligence to be use-case and application-aware to manage each individual’s requirement intelligently. MaxAI, MaxLinear’s native ML technology framework, is designed to solve this problem.
“At MaxLinear, we are not just integrating AI into our products—we’re innovating at the forefront of technology to create value for our customers and shareholders,” stated Will Torgerson, VP/GM Broadband Group at MaxLinear. “We believe MaxAI will be a significant growth driver for our company, strengthening our competitive position and contributing to our financial success. By delivering unparalleled performance and efficiency, we’re enabling service providers to differentiate their offering in a crowded market.”
MaxLinear will showcase the implementation of this technology on its AnyWAN-based Wi-Fi 7 platform at Network X 2024 in Paris. The reference platform used in this demonstration features the MaxLinear’s AnyWAN URX852 universal, ultra-scalable network processor SoC and the MaxLinear MxL31712 Wi-Fi 7 radio that integrates the tri-band 4+4+4 MAC plus RF in a single chip.
Key features of the demo include:
- Hardware-Optimized, Native-ML, On-device AI: MaxLinear will demonstrate the versatility of its gateway platform by running a demanding Wi-Fi multi-user application while also running the MaxAI native-ML framework in parallel. This demonstration will also show MaxAI in action including intelligent, application-aware traffic management, and overall robustness in responding to challenging RF and bandwidth constraints in real-time while keeping the application running smoothly.
- Natural Language Interaction: Enables interaction using natural voice, support for user queries, application prioritization, and application performance/debug-related queries etc. with real-time voice responses.
- Intelligent Diagnostics and Log Collection on Demand: Demonstrates automated debug and log collection for transmission to relevant Level 1/2 customer support if needed. This alleviates the need for additional handholding for an end user and saves MSO and service provider support costs.
Industry sources further added that the MaxAI technology framework has been architected to be scalable and flexible. The APIs allow for application intersection at multiple layers depending on one’s needs. From choosing curated, pre-trained recipes with on-device computing to supporting more advanced, compute-intensive implementations leveraging on platform or cloud computing, the framework provides implementation flexibility at any layer/compute configuration.
