Firmenblog über Supply Lattice CrossLink-NX Embedded Vision and Processing FPGA:LIFCL-17,LIFCL-33,LIFCL-40

Supply Lattice CrossLink-NX Embedded Vision and Processing FPGA:LIFCL-17,LIFCL-33,LIFCL-40

Shenzhen Mingjiada Electronics Co., Ltd., as an authorised independent distributor of globally renowned electronic components, leverages its many years of industry experience and stable supply chain system to provide customers with comprehensive electronic component solutions.

Supply Advantages

1. Comprehensive range of chips: covering multiple sectors

MCU/MPU: General-purpose, low-power, automotive-grade

Memory chips: DRAM (DDR3/4/5), NAND, NOR, EEPROM, eMMC, UFS, SSD

Communications/RF: 5G, Bluetooth, WiFi, GNSS, satellite communications, Starlink-related chips

Automotive/Industrial Grade: Automotive-grade MCUs, SoCs, power management, IGBTs, power devices

Analogue/Mixed-Signal: ADCs/DACs, operational amplifiers, power management, interface chips

AI/Specialised Chips: FPGAs, processors, sensors, optical modules

Application Areas: 5G, new energy, IoT, automotive electronics, medical, industrial control, communication base stations, AI, smart wearables, aerospace

2. Supply Chain and Inventory: Ample stock, rapid dispatch

Extensive inventory: Over 2 million SKUs in stock

Flexible order quantities: Supports small-batch samples and large-volume orders

Global delivery: Global logistics network, multi-currency settlement, covering Asia-Pacific, Europe and the Americas

3. Quality and Assurance: Genuine OEM products, professional certification

100% Genuine Products: Strict channel control to eliminate refurbished or loose new items

Automotive/Industrial-Grade Certification: Extensive product range compliant with AEC-Q100/101 and ASIL-B/D safety standards

Stable Supply Channels: Long-term partnerships with manufacturers and distributors, offering significant advantages in sourcing scarce models

One-Stop Order Fulfilment: Comprehensive coverage of Bill of Materials (BOM) across all categories, reducing supplier management

I. CrossLink-NX Series Core Foundation: Key Advantages of the Nexus Platform Architecture

The entire Lattice CrossLink-NX series of FPGAs is built using the industry-leading 28nm FD-SOI (Floating-Domain Silicon-on-Insulator) process. Optimised through the proprietary Nexus FPGA architecture, the design addresses the core challenges of embedded edge vision scenarios from the underlying process technology to architectural adaptation, delivering a multi-dimensional leap forward compared to competing FPGAs in the same class. In terms of power consumption control, this series of devices achieves power savings of up to 75% compared to traditional FPGAs of the same class. This makes them perfectly suited for power-sensitive scenarios such as battery-powered portable vision devices, long-term in-vehicle monitoring terminals, and industrial low-power sensing and data acquisition nodes. There is no need to add complex cooling or power regulation circuits, significantly simplifying the overall hardware design architecture. In terms of reliability, the FD-SOI process significantly reduces the device’s soft error rate and offers outstanding resistance to electromagnetic interference and extreme temperature fluctuations. This enables long-term, stable, and uninterrupted operation in harsh industrial environments, complex automotive electromagnetic conditions, and all-weather outdoor security scenarios, meeting the fundamental reliability certification requirements for industrial and automotive grades.

In terms of hardware architecture, the CrossLink-NX series features a core characteristic of an ultra-high storage-to-logic ratio, with up to 170 bits of storage per logic unit. It incorporates large-capacity on-chip RAM resources, enabling core operations such as image frame buffering, pixel data pre-processing, and temporary storage of intermediate AI inference data without the need for frequent access to external storage. This effectively reduces data transmission latency and enhances the real-time performance of visual processing. Furthermore, the device integrates a comprehensive range of dedicated hard-wired interfaces, eliminating the need for additional external interface conversion chips. It natively supports 2.5Gbps MIPI D-PHY, 5Gbps PCIe, 1.5Gbps programmable I/O and 1066Mbps DDR3 high-speed memory interfaces, and is compatible with mainstream vision transmission protocols such as LVDS, subLVDS and OpenLDI. It provides a one-stop solution for full-link vision data interaction, including image sensor signal acquisition, video data bridging, multi-channel image stitching and display terminal driving, truly realising the integration of vision interface conversion, data pre-processing and edge computing acceleration within a single chip. In terms of packaging, it caters to the demand for extreme miniaturisation, with a minimum package size of just 4mm × 4mm. This makes it suitable for the PCB layouts of micro-embedded hardware, meeting the design requirements of space-constrained scenarios such as portable smart devices, small industrial sensor modules and concealed in-vehicle vision terminals.

II. Precise positioning of three core models: LIFCL-17, LIFCL-33 and LIFCL-40 with graduated hardware specifications

The LIFCL-17, LIFCL-33 and LIFCL-40, are designed with a core logic of progressively increasing logic cell counts, synchronous expansion of memory resources and DSP processing power, and tiered upgrades in interface expansion capabilities. They precisely cover three tiers: entry-level basic applications, mid-range mainstream scenarios, and high-end complex vision processing. Users can flexibly select models based on the number of connected sensors, image processing complexity, and AI inference processing power requirements, avoiding waste from redundant hardware resources whilst balancing cost and performance. The three devices share a common architecture, utilise the same design tools, and are compatible with the same software ecosystem. Hardware pins and code can be rapidly migrated and reused, significantly reducing R&D costs and development cycles for product iterations and upgrades.

1. The Preferred Choice for Entry-Level, Lightweight Applications: LIFCL-17 FPGA

As the entry-level model in the CrossLink-NX series, the LIFCL-17 features a core configuration of 17K logic cells. With streamlined hardware specifications, it prioritises exceptional value for money and ultra-low power consumption, and is specifically tailored for single-channel embedded vision processing scenarios. This model features built-in basic on-chip RAM and an entry-level DSP processing unit. Without the need for complex computational configurations, it can efficiently handle basic vision pre-processing tasks such as single-channel MIPI image sensor data acquisition, simple video format conversion, basic image signal denoising, and single-channel video bridging and forwarding. It can also undertake lightweight edge AI detection and inference tasks, enabling low-computational-power intelligent analysis functions such as object counting and simple motion detection. With a wide range of compact package options, it is suitable for various compact PCB layouts. It boasts the lowest power consumption in the entire product range, making it particularly suitable for cost-sensitive, feature-streamlined and space-constrained entry-level applications such as battery-powered portable vision devices, small home smart security cameras, single-channel industrial position detection sensor modules, and vision-assisted modules for smart home appliances. For projects requiring only basic vision data acquisition and simple data forwarding/pre-processing, the LIFCL-17 perfectly replaces traditional FPGA + external interface chip combinations with the lowest hardware cost and power consumption, simplifying system architecture and shortening development cycles.

2. Mid-range Mainstream All-rounder: LIFCL-33 FPGA

The LIFCL-33 is the flagship mid-range model in the CrossLink-NX series, featuring an upgraded logic cell count of 33K. Building upon the LIFCL-17, it comprehensively expands on-chip memory resources, DSP processing units and the number of programmable I/O interfaces, achieving an optimal balance between performance, power consumption and cost. It is currently the most widely adopted mainstream model in mass-produced embedded vision projects. This model supports the synchronous aggregation of multiple image sensors and can simultaneously receive MIPI CSI-2 image data from multiple sources, enabling mid-range visual processing tasks such as multi-channel image stitching, synchronous image alignment, real-time cropping and scaling of high-definition video, and image quality enhancement pre-processing. In terms of edge AI computing power, ample DSP resources support lightweight inference for medium-scale deep learning models, meeting routine edge intelligent vision requirements such as basic defect detection of industrial workpieces, in-vehicle driving assistance visual monitoring, and human figure recognition in commercial security areas. Combining excellent scalability with stability, it is compatible with a wide range of industrial and automotive operating temperature conditions. Whilst retaining the core advantages of the CrossLink-NX series—ultra-low power consumption and a compact package—it addresses the shortcomings of entry-level models in terms of computing power and interface capabilities. Suitable for the vast majority of mainstream mass-production scenarios in industrial embedded vision, lightweight automotive perception, and commercial smart security, it is an all-round choice that balances practicality and cost-effectiveness.

3. High-end, high-performance flagship model: LIFCL-40 FPGA

As the high-end flagship model of the CrossLink-NX series, the LIFCL-40 features 40K top-tier logic cells, the largest on-chip RAM capacity in the series (up to 3MB), the most comprehensive DSP computational resources, and the highest number of high-speed hard-core interfaces. It is specifically designed for high-end, complex application scenarios such as multi-channel concurrent vision processing, real-time high-resolution video processing, and high-precision complex edge AI inference. This model supports synchronous aggregation and integrated processing of data from up to 13 MIPI image sensors, enabling high-intensity computational tasks such as real-time 4K HD video encoding and decoding, seamless multi-view stitching and fusion, high-precision image feature extraction, and parallel acceleration of complex vision algorithms. In the field of edge AI inference, ample logic and computing resources support the operation of high-precision, large-scale lightweight neural network models, meeting the computational demands of high-demand scenarios such as industrial high-precision defect detection, in-vehicle multi-camera panoramic surround view stitching, real-time multi-target recognition and tracking in intelligent transportation, and high-end commercial smart vision terminals. Although a flagship model, it retains the Nexus platform’s core low-power characteristics, offering significant power-saving advantages over traditional FPGAs of comparable performance. Requiring no additional thermal management design, it balances exceptional processing performance with the requirements for compact device size and low-power operation, making it the premier choice for high-end embedded vision and high-performance edge processing scenarios.

III. Three Core Application Scenarios: Catering to Embedded Vision and Edge Processing Needs Across All Industries

1. Embedded Vision Sensor Aggregation and Video Bridging

Multi-sensor connectivity and video signal bridging represent the most fundamental and core application scenarios for the three FPGAs in the CrossLink-NX series. Leveraging native high-speed vision-specific hard interfaces such as MIPI and LVDS, they enable seamless data bridging between various image sensors, display terminals and host processors without the need for external conversion chips. The LIFCL-17 is designed for simple one-to-one bridging of a single sensor, meeting the basic visual signal transmission requirements of small devices; the LIFCL-33 supports the synchronous aggregation and signal forwarding of 2–4 sensors, catering to mainstream industrial and security scenarios involving multi-channel basic data acquisition; The LIFCL-40 enables large-scale data aggregation from ten or more sensors and the stitching and integration of image data, providing a unified, standardised video data stream to the backend host controller. This significantly reduces the data processing load on the host processor and simplifies the overall system architecture, making it widely applicable in industrial vision sensor arrays, in-vehicle multi-camera acquisition modules, and multi-channel video acquisition terminals for security applications.

2. Real-time Image Pre-processing and Image Quality Algorithm Acceleration

Leveraging their high memory-to-logic ratio and ample DSP resources, these three FPGA models can independently handle various image pre-processing algorithm hardware acceleration tasks without relying on the main CPU. This includes fundamental image quality processing operations such as image denoising, colour correction, white balance adjustment, image cropping and scaling, frame rate conversion, and distortion correction. The hardware parallel processing architecture offers lower latency and greater real-time performance compared to software-based CPU processing. The entry-level LIFCL-17 meets basic image quality optimisation requirements; the mid-range LIFCL-33 is suited to routine pre-processing of high-definition video; and the high-end LIFCL-40 supports real-time complex image quality calibration and multi-view synchronous processing for 4K high-definition video. This effectively enhances imaging quality, providing high-quality raw data support for subsequent AI inference and image recognition, and is widely applicable to scenarios such as industrial vision inspection, in-vehicle image capture, and imaging optimisation for smart camera devices.

3. Offloading Lightweight AI Inference Computational Power at the Edge

Addressing the pain points of insufficient computing power in the main CPU of embedded edge devices, high AI inference latency and high power consumption, the three FPGAs in the CrossLink-NX series can serve as dedicated AI computing offload accelerators. They handle lightweight deep learning model inference tasks at the edge, enabling intelligent analysis functions such as object detection, object counting, human figure recognition and defect classification. This allows for real-time intelligent decision-making locally, without relying on cloud computing power. The LIFCL-17 is suited to low-computational-load AI tasks such as simple counting and basic detection; The LIFCL-33 supports mainstream AI applications such as standard object recognition and basic classification detection; the LIFCL-40 is capable of handling high-computational-demand edge AI requirements, such as high-precision multi-object recognition and intelligent analysis of complex operating conditions. With flexible computational resource allocation, controllable power consumption and convenient deployment, these devices are perfectly suited for embedded edge AI scenarios such as industrial edge intelligent inspection, in-vehicle edge perception and early warning, and intelligent security early warning and image capture.