AI Inference at the Rugged Edge:
Meeting Edge AI Performance with M.2 Accelerators
Why Download The Whitepaper?
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.
The Power of Edge AI
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.
Gartner predicts that by 2025, 75% of enterprise data will be processed outside a traditional centralized data center.
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)
System architects now widely believe the only path left for major improvements in the performance-energy-cost equation is the domain-specific approach integrating M.2 performance acceleration.
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.
Sample Inference Benchmarks
Popular Inference Accelerators for Your Workloads
- Multiple Cores
- Low Latency
- Serial Processing
- A handful of operations at once
- Large Memory Capacity
- More Flexible Programs
TPU & M.2 Modules
- Matrix Processing Hardware
- High Latency (Compared to CPU)
- Extreme Parallelism (Very High Throughput)
- Optimized for large batches
- Convolutional Neural Network (CNN)
- AI Focused Programs
- Thousands of Cores
- High Throughput
- Parallel Processing
- Thousands of operations at once
- Low Memory Capacity
- Less Flexible Programs
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
Military And Defense
Are you Rugged Edge ready?
Is your application rugged edge ready? When it comes to the rugged edge, the proper hardware is necessary to deliver the critical power performance to capture and analyze data in real time. Not only this, but rugged edge computers must operate without fault in the harshest environments where dust, debris, shock and vibration are common occurrences. Explore our Rugged Edge Media Hub that covers a wide range of topics to help you prepare for your edge AI application.
Rugged Edge Survival Guide eBook
This eBook will highlight how edge computing is moving technology and processing close to where data is generated from IoT sensors – and how rugged edge computing is advancing this strategy into more challenging physical surroundings with dedicated hardware technologies and strategies
Smaller, Better, Faster. M.2 Accelerators And Its Benefits For HPC And AI
Tune in to this episode of Rugged Edge Survival Guide Podcast with Premio’s Solutions Engineer, Peter Hsu, and Hailo’s Director of Americas Sales, Daryl Nees, to learn more about the benefits and use cases of edge AI applications as well as the hardware requirements to deploy AL and ML at the edge.
Powering Deep Learning and Inference Analysis in Heavy Industrial Applications
Rugged edge computing accelerates data processing based on sensor input data, enabling access and analytics close to the data source. To achieve that in industrial settings, machine learning is required but must be supported by dedicated hardware.
Podcast: Connecting the ‘Near Edge’ & ‘Far Edge’ with Dedicated Computing Hardware
In this episode of Rugged Edge Survival Guide Live Podcast, Dustin Seetoo, Director of Product Marketing here at Premio Inc., provides thought leadership into the convergence of the 3 key technologies that are shaping edge computing as well as connecting the ‘near edge’ and ‘far edge’ with purpose-built computing solutions.
Edge AI: The Next Generation of Artificial Intelligence for AIoT applications
Discover edge AI technologies and robust hardware. Learn more about how edge AI transforms industrial applications at the rugged edge!
What Is The M.2 Expansion Slot? The Future Of Compact, Robust Technologies.
Learn more about the technologies behind M.2 interface!
Data-rich computing: M.2 meets AI at the edge
As the demand to meet price-performance-power in industrial AI data driven processes becomes more rigorous, see how M.2 is looking to accelerate performance.
Explore Rugged Edge AI Inference Solutions
Specialized hardware is required to deliver the critical compute power to capture, process, and analyze data in real time for AI edge applications. Check out the videos to see how Premio's purpose-built rugged edge computers consolidate the next gen technologies in data processing, NVMe data storage, and 5G connectivity to bring machine intelligence to the edge.
AI Edge Inference Computers
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.