[Blog] Accelerating AI at the Edge (original) (raw)
Posted 09/10/2024 by Matt Dobrodziej, VP of Segment Marketing and Mark Hoopes, Director of Automotive & Industrial Segment Marketing
AI is evolving at a rapid pace not just from continued technology advancements, but also by various industries’ demands and requirements. With the surge of Large Language Models (LLMs) and Generative AI, the industry is grappling with the intense compute capabilities these cloud-based AI applications require to process big data, as well as train and deploy advanced AI models. Now, the industry is seeing AI being implemented into client devices including PCs and smartphones, and Edge applications in Automotive and Industrial equipment like robotics and medical devices, which are run on smaller language models at the Edge.
The Lattice team recently hosted a panel discussion with Bob O’Donnell, President and Chief Analyst at TECHnalysis Research, to discuss the emergence of the Edge AI era and the role of Field Programmable Gate Array (FPGA) based solutions in accelerating Edge AI adoption across industries. Watch the lively discussion that covers AI trends, real world Edge AI applications, and details about Lattice’s Edge AI-focused partnership with NVIDIA, and read on to explore FPGA’s important role in driving Edge AI innovation.
How Edge AI is Driving Innovation
While cloud-based AI provides greater computational capabilities and storage capacity, it requires significant processing power, energy consumption, higher network bandwidth, and incurs increased latency. By offloading processing tasks to local devices, Edge AI alleviates the burden on centralized servers and reduces operational costs. This improves energy efficiency by optimizing resource utilization and minimizing data transmission. Edge AI also offers a solution by keeping data localized, reducing exposure during transmission or storage, providing greater control over data for privacy and data protection.
Edge AI is becoming critical in revolutionizing the Industrial market, amongst others, serving as a key enabler for implementing real-time prediction and data processing in critical IoT applications like smart factories and robotics where automation is key. For example, in a manufacturing plant, autonomous machines must be able to execute tasks with the same accuracy and speed every time. These applications and environments require real-time networking and timing precision functionality because a multitude of sensors and other peripherals must be able to quickly exchange and process data to generate a comprehensive understanding of their environment.
However, despite the critical need for Edge AI and its vast potential, effective implementation is not without its challenges. Resource constraints, energy efficiency, and scalability all serve as hurdles for capitalizing on the benefits of Edge AI to drive impactful business outcomes. To address these obstacles, organizations are increasingly leveraging FPGA technology for its flexibility, low power consumption, parallel processing capabilities, and security.
The Role of FPGAs in AI at the Edge
With the growing demand and benefits of Edge AI, developers and organizations are looking to implement AI models into their applications – with speed, flexibility, and efficiency in mind. FPGAs are ideal for this task thanks to their low power sensor fusion processing and bridging capabilities.
Lattice FPGAs are vital for enabling scalable Edge AI applications due to their:
- Adaptability: FPGAs can be configured to perform specific AI tasks, which further enables developers to tailor applications to their unique needs. This helps boost reliability in performance by ensuring the Edge AI application is optimized for maximum efficiency.
- Scalability: FPGAs offer flexibility that is unmatched by fixed-function processors, allowing for post-deployment modifications and updates to ensure Edge AI devices remain relevant and effective even as requirements evolve. This is also critical for delivering derivative products into adjacent applications.
- Connectivity: FPGAs’ customizable I/O interfaces enable connectivity to a diverse array of Edge AI applications (e.g., cameras, lidar, radar, environmental sensors) across various environments. This helps reduce the complexity of leveraging several different sensors and SoCs, facilitating increased performance, adaptive interfaces, and bridging and sensor fusion processing.
- Low Latency/High Performance: FPGAs significantly reduce the latency between sensor data acquisition and processing, enabling faster responses and improved system performance. By performing preprocessing tasks and data aggregation on the FPGA, the Edge AI device’s compute engine can focus on more complex tasks, improving overall system efficiency, while simultaneously simplifying the main SoC workload, enabling reduced power and complexity within the main processor.
Growing Industry Ecosystem on Edge AI Implementation
As organizations increasingly execute processing with a system-level approach, Lattice has teamed up with NVIDIA to help accelerate AI implementation at the Edge. Last year, we announced a partnership and unveiled a new integrated solution that combines Lattice’s low power FPGAs with the NVIDIA Jetson Orin and IGX Orin platforms on an open-sourced reference board. It is designed to address developers’ needs for customizability, scalability, connectivity, and low latency when designing high-performance Edge AI applications.
The solution enables developers to rapidly design connectivity bridging applications from sensor to compute, while simplifying and accelerating the deployment of intelligent Edge systems that require diverse sensor input interfaces and protocols.
This FPGA-based sensor bridge reference design is implemented on the Lattice CertusPro™-NX Sensor to Ethernet Bridge Board that supports low latency, flexible sensor configuration and interfacing, and an Ethernet Packetizer . This design couples seamlessly with NVIDIA Holoscan sensor bridge software, offering easily programmable system control, ready to use configurable FPGA IP, and a full stack solution for data acquisition and processing leveraging NVIDIA Holoscan on NVIDIA IGX Orin and Orin AGX. This can be used in real-world settings to facilitate efficient sensor fusion that requires parallel processing and precise timing, as well as lidar and radar technologies that require situational awareness and object detection functionality.
Delivering Low Power AI with FPGAs
The integration of Lattice FPGA technology is pivotal in advancing low power AI at the Edge. As AI continues to evolve, the demand for efficient, scalable, and adaptable solutions becomes increasingly critical. FPGAs offer a unique combination of low power consumption, parallel processing capabilities, and flexibility, making them ideal for Edge AI applications.
By leveraging Lattice FPGAs , various industries can overcome the challenges of resource constraints, energy efficiency, and scalability. These programmable devices enable real-time data processing and prediction that is essential for applications in Industrial equipment, medical devices, automotive, and robotics. The adaptability of FPGAs allows for tailored AI solutions that meet the specific needs of various environments, ensuring optimal performance and reliability.
As we move forward, the role of FPGAs in delivering low power AI will continue to expand, providing developers with the tools needed to harness the full potential of Edge AI.
To learn more about how Lattice can enable AI at the Edge, contact our team today. To explore the cutting-edge trends, challenges and opportunities, and groundbreaking programmable solutions for applications like Edge AI, security, and advanced connectivity, register to attend Lattice Developers Conference on December 10-11, 2024.