Introduction
Edge computing brings data collection, processing, and storage closer to the source of data. This enables real-time data processing and facilitates faster actions and more accurate decisions. Edge AI utilizes edge computing and brings machine intelligence to the edge with specialized hardware. 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.
Key Takeaway
- The explosive amounts of data collected from IoT devices, advancement in processing power, and the improvement in programming codes together increases the quality of AI algorithms which is driving the adoption of Edge AI applications.
- Hardware acceleration is necessary to support CPU to improve processing power for advanced edge AI applications.
- Processing data at the edge reduces the cost companies are paying for Cloud services and reduces the latency for time sensitive applications such as surveillance, safety monitoring, and autonomous driving.
- Advancement in chip design and new hardware technology such as M.2 accelerators make running AI applications faster and more power efficient.
- Heterogeneous edge AI servers incorporates CPU, accelerator, and NVMe storage to provide the critical processing power and storage for edge AI applications.
Edge AI
Edge AI combines edge computing and Al algorithms to train machine learning models and enable AI applications right at the edge. Training and improving AI and machine learning algorithms require lots of data. In the past, the extensive amount of processing power that is required for AI and ML could only be found in a cloud data center. With the advancement and innovation in computer engineering and chip architecture design, AI processors become smaller, more power efficient, and generate less heat. This new architecture allows the chips to be implemented in edge servers and computers, which makes edge AI possible today. Edge AI enables faster and more accurate decisions with machine intelligence. With the increased speed and accuracy, advanced AI applications such as manufacturing robotics, autonomous driving, and machine vision are deployed to help increase productivity, operational efficiency, and worker’s safety.
Hardware Acceleration
The rapid evolution of AI and machine learning is fueling the demand for specialized hardware to support next-gen software algorithms. As Moore’s law slows down, CPU alone can no longer keep up with the processing power that is required for advanced Edge AI applications. Performance accelerators are designed to offload certain tasks from CPU, allowing the CPU to focus on critical applications and provide additional resources for processing advance AI and ML algorithms. Heterogeneous Edge AI servers incorporates CPU, accelerator, and NVMe storage that are designed and engineered to optimize AI and ML applications at the edge.
The balance between performance and power is key for hardware accelerators especially for Edge AI deployments. General purpose GPUs are commonly integrated to provide enormous processing power for advanced AI algorithms used for machine learning and intelligence. But although GPUs provide the much-needed performance boost, they are not optimized for edge deployments in remote and unstable environments. The size, power consumption, and heat management are some of the key drawbacks that generate additional operating costs on top of the upfront cost of the GPU itself. Application specific integrated circuits (ACIS) are designed to address these issues. Specialized accelerators such as TPUs and M.2 acceleration modules are new solutions that are compact, power efficient, and come purpose-built for driving performance in machine learning algorithms at the edge.
5G Ready Connectivity
The fifth generation of wireless network technology (5G) provides significantly faster speed, ultra-low latency, and exceptional reliability, which enable many AI applications that make critical decision in split seconds, such as autonomous driving and manufacturing robotics. Premio’s 5G ready AI Edge Inference computers are purpose-built to provide high-performance computing power, storage, and enables next-gen connectivity at the edge. Often times, edge computers are deployed under rugged conditions with constant shock and vibration as well as exposure to water and extreme temperatures. Overall, Edge AI will rely on both dedicated hardware acceleration and 5G connectivity in close proximity to data sensors for a new wave of machine intelligence and automation. Premio’s edge computing solutions are engineered and validated to sustain the rigors of such environments and can provide reliable consolidation of edge AI workloads.
How Does Premio Fit Into The Ecosystem?
Edge AI deployments require purpose-built hardware to drive reliable compute power and performance acceleration. Premio offers a comprehensive lineup of edge computing hardware solutions to address the needs for a wide range of applications. For over 30 year, Premio’s expertise in hardware engineering and manufacturing has helped our customers thrive across various industries. Our heterogeneous edge AI servers incorporates CPU, hardware accelerator, and NVMe storage to provide the critical processing power and storage for edge AI applications. Contact us today to talk to our rugged edge experts to find the best hardware solution for your AI Edge applications.
