AI Inference at the Rugged Edge: Meeting Edge AI Performance With M.2 Accelerators
The Power Of Edge AI
Data drives business innovation. It is the driving force behind the advancements that are made possible today. Data is everywhere, powers many applications, and provides people with value to deliver new services and make better decisions. And it is no exception in the industrial environment, where a large amount of data is captured, processed, and analyzed in real time. As automation grows, we are seeing the shift away from the cloud.
Edge AI has unlocked new potential for Industry 4.0 applications, and it is set to grow exponentially by 2025. Advancements made in the IoT devices, artificial intelligence, and edge computing have created a wide range of deployments around the world that utilize AI in edge applications. As AI and ML continue to utilize more IoT devices, edge solutions begin to hit a wall in data intensive workloads. Silicon evolution alone cannot support AI algorithms and the orders of magnitude greater processing performance they require. The necessary balance of performance, cost, and energy demands a new approach featuring more specialized domain-specific architectures.
Why Download The Whitepaper?
- Advancements in IoT devices, artificial intelligence, and edge computing have created a wide range of Edge AI deployments around the world that utilize AI in edge applications.
- The growth of Edge AI is generating a large demand for innovation in computing hardware to meet growing data workloads.
- M.2 Domain Specific Architectures, are unique hardware accelerators designed for specific workloads to help deliver the performance needed at the edge
- Benchmarks featuring HAILO-8™ DSA processors
Challenge: An Edge AI Bottleneck
Today, many areas of business are benefiting from the adoption of Edge AI. Edge AI is helping solve real world problems for many end users in a wide range of applications and industries. However, maximizing performance at the rugged edge is tricky and demanding. Harsh conditions restrict power efficiency, limit resources, and create a wall for traditional compute solutions to process data heavy workloads. As edge AI and the number of IoT and IIoT devices increase, the demand for purpose-built hardware that can optimize edge AI performance becomes critical.
Moore's Law is slowing, but the ability to maintain low power and energy efficiency does not. Power budgets, and mechanical and thermal performance face limits at the edge. A new demand for specialized hardware acceleration is looking to help alleviate the power restrictions seen at the edge. This paper explores the benefits of domain specific architectures, specifically ones using the M.2 form factor, that are designed to tackle very specific and demanding deep learning and inference workloads at the edge without exceeding total cost of ownership.
What Is M.2 & Domain Specific Architectures?
What Is M.2?
The M.2 is regarded as the Next Generation Form Factor and was developed by Intel to deliver peak performance and flexibility. As the successor of the mSATA and mPCIe, the M.2 interface is incredibly fast and versatile thanks to their ability to utilize full PCI Express lanes.
- Super compact module – Smallest M.2 devices are 18% smaller compared to smallest mPCIe devices.
- Flexible Measurements – some M.2 ports on a motherboard support multiple lengths of M.2 cards.
- Power-efficient – M.2 power consumption is limited to 7 watts (W).
- M.2 devices are much faster than SATA devices – around 50% to 650% faster.
- Blazing fast specification: NVMe protocol and PCIe 4.0 with up to x4 lanes (16Gb/s each lane)
Domain Specific Architectures
DSA, or Domain Specific Architecture, are pieces of performance acceleration hardware designed to take on defined AI workloads. DSAs are special in that they are very good at what they do and are customized for that specific workload. While traditional CPU And GPU systems can provide large processing power, they lack the necessary requirements needed to perform at the edge. DSAs can efficiently offer compact and power efficient solution to the harsh, unstable environments.
Rugged Edge Computing
DSAs help enable Edge AI to perform to the fullest. Localizing compute power allows for smart IoT devices such as cameras and sensors to efficiently capture and process data right at the source. Edge computing harnesses the processing power capabilities of DSAs in a rugged architecture to deliver real-time data analytics, enable AI, and provide trusted reliability all at the edge.
Key deployments where Rugged Edge Computing is demanded are: Industrial Automation, ADAS & Autonomous Vehicle, Surveillance & Security, Smart Kiosk.
Why Premio? Built Rugged. Built Ready.
Premio's line of purpose-built, rugged edge computers are designed and ready to tackle the necessary compute capabilities that drive Edge AI, even in the harshest conditions. Our line of Edge AI Inference and rugged edge computers provide next-gen computation capabilities to help deliver the necessary bandwidth to execute advanced AI algorithms. Built with the latest technologies in data storage, processing, and connectivity, our systems are certified to withstand all the extremities seen in the industrial world.
Our team is here to answer any questions you might have and help you work through specific compute challenges. Contact us to speak to one of our embedded computing experts today!