Product Name: Lattice sensAI™ Solution Stack: A Deterministic, Edge AI Inference Solution for Real Time Intelligence

Manufacturer: Lattice Semiconductor

Product Category: Computing Hardware, Software and Systems

Supporting Documentation (if available)

As artificial intelligence (AI) continues to move closer to the sensor, modern edge applications must deliver real-time intelligence under strict power, latency, and lifecycle constraints. Many far-edge environments cannot depend on cloud connectivity or power-hungry GPU-based architectures, yet still require deterministic performance, long operating lifecycles, and secure, upgradable designs. These challenges are especially critical in industrial, automotive, robotics, and embedded vision systems that operate continuously, often in thermal-constrained, space-limited, or intermittently connected environments.

The Lattice sensAI™ solution stack is a purpose-built, FPGA-based edge AI solution designed to address these far-edge requirements. Rather than acting as a standalone AI software tool, sensAI provides a complete, production-ready platform that integrates low power FPGA hardware, deterministic machine learning (ML) acceleration, deployment-accurate software tooling, a unified model ecosystem, and workflow automation to support end-to-end edge AI development and deployment. The solution is optimized for use cases where predictability, efficiency, and long-term stability are critical system requirements.

As AI algorithms and security regulations and risks are always changing, Lattice is consistently updating the Lattice sensAI solution stack to incorporate new, value-added feautres to help simplify and speed up customer design and deployment cycles. The latest sensAI release – version 8.0 - adds key new capabilities to address the latest needs for edge AI applications.At the compute level, the most recent version of Lattice sensAI is built around a redesigned ML accelerator optimized for Lattice low power FPGA architectures. The accelerator is co-designed with the compiler and software toolchain to ensure predictable latency, deterministic execution, and efficient resource utilization – key requirements for always-on, real-time workloads at the far edge. This hardware aware approach enables engineers to deploy modern computer vision and perception models that are designed specifically for edge deployment, rather than adapted from cloud-oriented architectures that often exhibit variable latency and high power consumption.
By offloading inference and sensor processing to low power FPGAs, Lattice sensAI supports sub-watt operation while maintaining real-time responsiveness. Unlike GPU or SoC-centric designs that can struggle with thermal dissipation and unpredictable scheduling behavior, FPGA-based acceleration enables consistent, repeatable performance under tight energy budgets. This makes the solution well suited for applications such as industrial inspection, defect detection, robotics navigation, automotive sensing, safety monitoring, and human-machine interface (HMI) systems that require reliable, low latency decision making directly at the point of sensing.
In addition to deterministic compute, the Lattice sensAI solution stack significantly expands support for purpose-built AI models optimized for deployment. The solution includes a unified, production-ready Model Zoo featuring pre-trained and validated models designed explicitly for edge execution. These include enhanced HMI models along with new models for multi-object detection and defect detection, enabling reliable perception across industrial automation, automotive systems, and embedded vision applications. Unlike experimental model collections intended for research, Lattice sensAI’s Model Zoo is designed to support version control, consistent documentation, and deployment repeatability. Models are optimized for deterministic latency, power efficiency, and privacy-preserving operation, simplifying system scaling across devices and product generations while supporting long lifecycle maintenance.

To reduce integration risk and development time, Lattice sensAI provides deployment-accurate quantization, simulation, and compilation tools that closely align training, validation, and on-device behavior. The toolchain supports fixed-point quantization, learned step-size quantization, and post-training quantization, enabling efficient execution on low power FPGA platforms while maintaining model accuracy. Bit-accurate simulation allows engineers to evaluate accuracy, latency, and FPGA resource utilization under hardware-realistic conditions prior to deployment, reducing costly design iterations and ensuring predictable real-time behavior once systems move into production.

Lattice sensAI also incorporates enhanced automation and interoperability features designed for scalable production environments. YAML-based automation enables repeatable system specification, configuration management, and rapid prototyping, while a simplified RISC-V® codebase allows engineers to customize embedded control logic and system behavior for specific applications. Python API integration improves interoperability with existing AI development workflows, helping teams bridge model development, system integration, and deployment pipelines more efficiently. These capabilities support collaboration across machine learning, hardware, and embedded software teams, an essential requirement for safety-sensitive, regulated, and long-lifecycle edge applications.

Security and long-term deployment stability are integral to the sensAI architecture. The solution stack is designed with security and in-field upgradability in mind, enabling deployed systems to evolve over extended lifetimes without compromising system integrity or reliability. These security and lifecycle features help protect intellectual property, ensure trusted execution, and support post-deployment enhancements as application requirements change. To further reduce architectural risk, sensAI is complemented by a validated Golden AI Reference Design (GARD) that integrates camera interfaces, memory subsystems, ML acceleration, and embedded control into cohesive, system-level platforms, allowing customers to move from evaluation to production more quickly and confidently.

The practical value of the Lattice sensAI solution stack is demonstrated through real-world customer adoption and industry collaboration. Following the latest release of Lattice sensAI, Mitsubishi Electric highlighted its collaboration with Lattice to jointly develop edge AI solutions for industrial equipment, emphasizing that the combination of FPGA-based AI acceleration and industrial domain expertise enables scalable, secure, and production-ready automation systems. This validation underscores sensAI’s suitability for demanding far-edge environments where reliability, energy efficiency, and long-term support are essential.

By integrating deterministic compute, production-ready models, deployment-accurate tooling, automation, security, and validated reference designs into a cohesive architecture, the Lattice sensAI solution stack functions as a complete edge AI solution rather than a collection of isolated components. Purpose-built for far-edge deployments, it enables design engineers, system integrators, and end users to more quickly deploy scalable, secure, and real-time AI intelligence directly at the point of sensing, where power efficiency, predictability, and lifecycle resilience matter most.